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Merge branch 'master' of https://github.com/erwincoumans/bullet3

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Erwin Coumans 2017-11-16 10:48:37 -08:00
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21
examples/pybullet/gym/pybullet_envs/agents/__init__.py
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@ -1,2 +1,23 @@
# Copyright 2017 The TensorFlow Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Executable scripts for reinforcement learning."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from . import train_ppo
from . import utility
from . import visualize

27
examples/pybullet/gym/pybullet_envs/agents/configs.py
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@ -20,21 +20,21 @@ from __future__ import print_function
import functools
from agents import ppo
from agents.scripts import networks
from . import ppo
from . import networks
from pybullet_envs.bullet import minitaur_gym_env
from pybullet_envs.bullet import minitaur_duck_gym_env
from pybullet_envs.bullet import minitaur_env_randomizer
import pybullet_envs.bullet.minitaur_gym_env as minitaur_gym_env
import pybullet_envs
import tensorflow as tf
def default():
"""Default configuration for PPO."""
# General
algorithm = ppo.PPOAlgorithm
num_agents = 10
eval_episodes = 20
num_agents = 30
eval_episodes = 30
use_gpu = False
# Network
network = networks.feed_forward_gaussian
@ -44,18 +44,17 @@ def default():
value=r'.*/value/.*')
policy_layers = 200, 100
value_layers = 200, 100
init_mean_factor = 0.05
init_mean_factor = 0.1
init_logstd = -1
# Optimization
update_every = 20
policy_optimizer = 'AdamOptimizer'
value_optimizer = 'AdamOptimizer'
update_epochs_policy = 50
update_epochs_value = 50
policy_lr = 1e-4
value_lr = 3e-4
update_every = 30
update_epochs = 25
optimizer = tf.train.AdamOptimizer
update_epochs_policy = 64
update_epochs_value = 64
learning_rate = 1e-4
# Losses
discount = 0.985
discount = 0.995
kl_target = 1e-2
kl_cutoff_factor = 2
kl_cutoff_coef = 1000

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examples/pybullet/gym/pybullet_envs/agents/networks.py Normal file
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# Copyright 2017 The TensorFlow Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Network definitions for the PPO algorithm."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import functools
import operator
import tensorflow as tf
NetworkOutput = collections.namedtuple(
'NetworkOutput', 'policy, mean, logstd, value, state')
def feed_forward_gaussian(
config, action_size, observations, unused_length, state=None):
"""Independent feed forward networks for policy and value.
The policy network outputs the mean action and the log standard deviation
is learned as independent parameter vector.
Args:
config: Configuration object.
action_size: Length of the action vector.
observations: Sequences of observations.
unused_length: Batch of sequence lengths.
state: Batch of initial recurrent states.
Returns:
NetworkOutput tuple.
"""
mean_weights_initializer = tf.contrib.layers.variance_scaling_initializer(
factor=config.init_mean_factor)
logstd_initializer = tf.random_normal_initializer(config.init_logstd, 1e-10)
flat_observations = tf.reshape(observations, [
tf.shape(observations)[0], tf.shape(observations)[1],
functools.reduce(operator.mul, observations.shape.as_list()[2:], 1)])
with tf.variable_scope('policy'):
x = flat_observations
for size in config.policy_layers:
x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
mean = tf.contrib.layers.fully_connected(
x, action_size, tf.tanh,
weights_initializer=mean_weights_initializer)
logstd = tf.get_variable(
'logstd', mean.shape[2:], tf.float32, logstd_initializer)
logstd = tf.tile(
logstd[None, None],
[tf.shape(mean)[0], tf.shape(mean)[1]] + [1] * (mean.shape.ndims - 2))
with tf.variable_scope('value'):
x = flat_observations
for size in config.value_layers:
x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
value = tf.contrib.layers.fully_connected(x, 1, None)[..., 0]
mean = tf.check_numerics(mean, 'mean')
logstd = tf.check_numerics(logstd, 'logstd')
value = tf.check_numerics(value, 'value')
policy = tf.contrib.distributions.MultivariateNormalDiag(
mean, tf.exp(logstd))
return NetworkOutput(policy, mean, logstd, value, state)
def recurrent_gaussian(
config, action_size, observations, length, state=None):
"""Independent recurrent policy and feed forward value networks.
The policy network outputs the mean action and the log standard deviation
is learned as independent parameter vector. The last policy layer is
recurrent and uses a GRU cell.
Args:
config: Configuration object.
action_size: Length of the action vector.
observations: Sequences of observations.
length: Batch of sequence lengths.
state: Batch of initial recurrent states.
Returns:
NetworkOutput tuple.
"""
mean_weights_initializer = tf.contrib.layers.variance_scaling_initializer(
factor=config.init_mean_factor)
logstd_initializer = tf.random_normal_initializer(config.init_logstd, 1e-10)
cell = tf.contrib.rnn.GRUBlockCell(config.policy_layers[-1])
flat_observations = tf.reshape(observations, [
tf.shape(observations)[0], tf.shape(observations)[1],
functools.reduce(operator.mul, observations.shape.as_list()[2:], 1)])
with tf.variable_scope('policy'):
x = flat_observations
for size in config.policy_layers[:-1]:
x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
x, state = tf.nn.dynamic_rnn(cell, x, length, state, tf.float32)
mean = tf.contrib.layers.fully_connected(
x, action_size, tf.tanh,
weights_initializer=mean_weights_initializer)
logstd = tf.get_variable(
'logstd', mean.shape[2:], tf.float32, logstd_initializer)
logstd = tf.tile(
logstd[None, None],
[tf.shape(mean)[0], tf.shape(mean)[1]] + [1] * (mean.shape.ndims - 2))
with tf.variable_scope('value'):
x = flat_observations
for size in config.value_layers:
x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
value = tf.contrib.layers.fully_connected(x, 1, None)[..., 0]
mean = tf.check_numerics(mean, 'mean')
logstd = tf.check_numerics(logstd, 'logstd')
value = tf.check_numerics(value, 'value')
policy = tf.contrib.distributions.MultivariateNormalDiag(
mean, tf.exp(logstd))
# assert state.shape.as_list()[0] is not None
return NetworkOutput(policy, mean, logstd, value, state)

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examples/pybullet/gym/pybullet_envs/agents/ppo/__init__.py Normal file
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@ -0,0 +1,21 @@
# Copyright 2017 The TensorFlow Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Proximal Policy Optimization algorithm."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from .algorithm import PPOAlgorithm

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examples/pybullet/gym/pybullet_envs/agents/ppo/algorithm.py Normal file
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@ -0,0 +1,515 @@
# Copyright 2017 The TensorFlow Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Proximal Policy Optimization algorithm.
Based on John Schulman's implementation in Python and Theano:
https://github.com/joschu/modular_rl/blob/master/modular_rl/ppo.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import tensorflow as tf
from . import memory
from . import normalize
from . import utility
class PPOAlgorithm(object):
"""A vectorized implementation of the PPO algorithm by John Schulman."""
def __init__(self, batch_env, step, is_training, should_log, config):
"""Create an instance of the PPO algorithm.
Args:
batch_env: In-graph batch environment.
step: Integer tensor holding the current training step.
is_training: Boolean tensor for whether the algorithm should train.
should_log: Boolean tensor for whether summaries should be returned.
config: Object containing the agent configuration as attributes.
"""
self._batch_env = batch_env
self._step = step
self._is_training = is_training
self._should_log = should_log
self._config = config
self._observ_filter = normalize.StreamingNormalize(
self._batch_env.observ[0], center=True, scale=True, clip=5,
name='normalize_observ')
self._reward_filter = normalize.StreamingNormalize(
self._batch_env.reward[0], center=False, scale=True, clip=10,
name='normalize_reward')
# Memory stores tuple of observ, action, mean, logstd, reward.
template = (
self._batch_env.observ[0], self._batch_env.action[0],
self._batch_env.action[0], self._batch_env.action[0],
self._batch_env.reward[0])
self._memory = memory.EpisodeMemory(
template, config.update_every, config.max_length, 'memory')
self._memory_index = tf.Variable(0, False)
use_gpu = self._config.use_gpu and utility.available_gpus()
with tf.device('/gpu:0' if use_gpu else '/cpu:0'):
# Create network variables for later calls to reuse.
action_size = self._batch_env.action.shape[1].value
self._network = tf.make_template(
'network', functools.partial(config.network, config, action_size))
output = self._network(
tf.zeros_like(self._batch_env.observ)[:, None],
tf.ones(len(self._batch_env)))
with tf.variable_scope('ppo_temporary'):
self._episodes = memory.EpisodeMemory(
template, len(batch_env), config.max_length, 'episodes')
if output.state is None:
self._last_state = None
else:
# Ensure the batch dimension is set.
tf.contrib.framework.nest.map_structure(
lambda x: x.set_shape([len(batch_env)] + x.shape.as_list()[1:]),
output.state)
# pylint: disable=undefined-variable
self._last_state = tf.contrib.framework.nest.map_structure(
lambda x: tf.Variable(lambda: tf.zeros_like(x), False),
output.state)
self._last_action = tf.Variable(
tf.zeros_like(self._batch_env.action), False, name='last_action')
self._last_mean = tf.Variable(
tf.zeros_like(self._batch_env.action), False, name='last_mean')
self._last_logstd = tf.Variable(
tf.zeros_like(self._batch_env.action), False, name='last_logstd')
self._penalty = tf.Variable(
self._config.kl_init_penalty, False, dtype=tf.float32)
self._optimizer = self._config.optimizer(self._config.learning_rate)
def begin_episode(self, agent_indices):
"""Reset the recurrent states and stored episode.
Args:
agent_indices: Tensor containing current batch indices.
Returns:
Summary tensor.
"""
with tf.name_scope('begin_episode/'):
if self._last_state is None:
reset_state = tf.no_op()
else:
reset_state = utility.reinit_nested_vars(
self._last_state, agent_indices)
reset_buffer = self._episodes.clear(agent_indices)
with tf.control_dependencies([reset_state, reset_buffer]):
return tf.constant('')
def perform(self, agent_indices, observ):
"""Compute batch of actions and a summary for a batch of observation.
Args:
agent_indices: Tensor containing current batch indices.
observ: Tensor of a batch of observations for all agents.
Returns:
Tuple of action batch tensor and summary tensor.
"""
with tf.name_scope('perform/'):
observ = self._observ_filter.transform(observ)
if self._last_state is None:
state = None
else:
state = tf.contrib.framework.nest.map_structure(
lambda x: tf.gather(x, agent_indices), self._last_state)
output = self._network(observ[:, None], tf.ones(observ.shape[0]), state)
action = tf.cond(
self._is_training, output.policy.sample, lambda: output.mean)
logprob = output.policy.log_prob(action)[:, 0]
# pylint: disable=g-long-lambda
summary = tf.cond(self._should_log, lambda: tf.summary.merge([
tf.summary.histogram('mean', output.mean[:, 0]),
tf.summary.histogram('std', tf.exp(output.logstd[:, 0])),
tf.summary.histogram('action', action[:, 0]),
tf.summary.histogram('logprob', logprob)]), str)
# Remember current policy to append to memory in the experience callback.
if self._last_state is None:
assign_state = tf.no_op()
else:
assign_state = utility.assign_nested_vars(
self._last_state, output.state, agent_indices)
with tf.control_dependencies([
assign_state,
tf.scatter_update(
self._last_action, agent_indices, action[:, 0]),
tf.scatter_update(
self._last_mean, agent_indices, output.mean[:, 0]),
tf.scatter_update(
self._last_logstd, agent_indices, output.logstd[:, 0])]):
return tf.check_numerics(action[:, 0], 'action'), tf.identity(summary)
def experience(
self, agent_indices, observ, action, reward, unused_done, unused_nextob):
"""Process the transition tuple of the current step.
When training, add the current transition tuple to the memory and update
the streaming statistics for observations and rewards. A summary string is
returned if requested at this step.
Args:
agent_indices: Tensor containing current batch indices.
observ: Batch tensor of observations.
action: Batch tensor of actions.
reward: Batch tensor of rewards.
unused_done: Batch tensor of done flags.
unused_nextob: Batch tensor of successor observations.
Returns:
Summary tensor.
"""
with tf.name_scope('experience/'):
return tf.cond(
self._is_training,
# pylint: disable=g-long-lambda
lambda: self._define_experience(
agent_indices, observ, action, reward), str)
def _define_experience(self, agent_indices, observ, action, reward):
"""Implement the branch of experience() entered during training."""
update_filters = tf.summary.merge([
self._observ_filter.update(observ),
self._reward_filter.update(reward)])
with tf.control_dependencies([update_filters]):
if self._config.train_on_agent_action:
# NOTE: Doesn't seem to change much.
action = self._last_action
batch = (
observ, action, tf.gather(self._last_mean, agent_indices),
tf.gather(self._last_logstd, agent_indices), reward)
append = self._episodes.append(batch, agent_indices)
with tf.control_dependencies([append]):
norm_observ = self._observ_filter.transform(observ)
norm_reward = tf.reduce_mean(self._reward_filter.transform(reward))
# pylint: disable=g-long-lambda
summary = tf.cond(self._should_log, lambda: tf.summary.merge([
update_filters,
self._observ_filter.summary(),
self._reward_filter.summary(),
tf.summary.scalar('memory_size', self._memory_index),
tf.summary.histogram('normalized_observ', norm_observ),
tf.summary.histogram('action', self._last_action),
tf.summary.scalar('normalized_reward', norm_reward)]), str)
return summary
def end_episode(self, agent_indices):
"""Add episodes to the memory and perform update steps if memory is full.
During training, add the collected episodes of the batch indices that
finished their episode to the memory. If the memory is full, train on it,
and then clear the memory. A summary string is returned if requested at
this step.
Args:
agent_indices: Tensor containing current batch indices.
Returns:
Summary tensor.
"""
with tf.name_scope('end_episode/'):
return tf.cond(
self._is_training,
lambda: self._define_end_episode(agent_indices), str)
def _define_end_episode(self, agent_indices):
"""Implement the branch of end_episode() entered during training."""
episodes, length = self._episodes.data(agent_indices)
space_left = self._config.update_every - self._memory_index
use_episodes = tf.range(tf.minimum(
tf.shape(agent_indices)[0], space_left))
episodes = [tf.gather(elem, use_episodes) for elem in episodes]
append = self._memory.replace(
episodes, tf.gather(length, use_episodes),
use_episodes + self._memory_index)
with tf.control_dependencies([append]):
inc_index = self._memory_index.assign_add(tf.shape(use_episodes)[0])
with tf.control_dependencies([inc_index]):
memory_full = self._memory_index >= self._config.update_every
return tf.cond(memory_full, self._training, str)
def _training(self):
"""Perform multiple training iterations of both policy and value baseline.
Training on the episodes collected in the memory. Reset the memory
afterwards. Always returns a summary string.
Returns:
Summary tensor.
"""
with tf.name_scope('training'):
assert_full = tf.assert_equal(
self._memory_index, self._config.update_every)
with tf.control_dependencies([assert_full]):
data = self._memory.data()
(observ, action, old_mean, old_logstd, reward), length = data
with tf.control_dependencies([tf.assert_greater(length, 0)]):
length = tf.identity(length)
observ = self._observ_filter.transform(observ)
reward = self._reward_filter.transform(reward)
update_summary = self._perform_update_steps(
observ, action, old_mean, old_logstd, reward, length)
with tf.control_dependencies([update_summary]):
penalty_summary = self._adjust_penalty(
observ, old_mean, old_logstd, length)
with tf.control_dependencies([penalty_summary]):
clear_memory = tf.group(
self._memory.clear(), self._memory_index.assign(0))
with tf.control_dependencies([clear_memory]):
weight_summary = utility.variable_summaries(
tf.trainable_variables(), self._config.weight_summaries)
return tf.summary.merge([
update_summary, penalty_summary, weight_summary])
def _perform_update_steps(
self, observ, action, old_mean, old_logstd, reward, length):
"""Perform multiple update steps of value function and policy.
The advantage is computed once at the beginning and shared across
iterations. We need to decide for the summary of one iteration, and thus
choose the one after half of the iterations.
Args:
observ: Sequences of observations.
action: Sequences of actions.
old_mean: Sequences of action means of the behavioral policy.
old_logstd: Sequences of action log stddevs of the behavioral policy.
reward: Sequences of rewards.
length: Batch of sequence lengths.
Returns:
Summary tensor.
"""
return_ = utility.discounted_return(
reward, length, self._config.discount)
value = self._network(observ, length).value
if self._config.gae_lambda:
advantage = utility.lambda_return(
reward, value, length, self._config.discount,
self._config.gae_lambda)
else:
advantage = return_ - value
mean, variance = tf.nn.moments(advantage, axes=[0, 1], keep_dims=True)
advantage = (advantage - mean) / (tf.sqrt(variance) + 1e-8)
advantage = tf.Print(
advantage, [tf.reduce_mean(return_), tf.reduce_mean(value)],
'return and value: ')
advantage = tf.Print(
advantage, [tf.reduce_mean(advantage)],
'normalized advantage: ')
# pylint: disable=g-long-lambda
value_loss, policy_loss, summary = tf.scan(
lambda _1, _2: self._update_step(
observ, action, old_mean, old_logstd, reward, advantage, length),
tf.range(self._config.update_epochs),
[0., 0., ''], parallel_iterations=1)
print_losses = tf.group(
tf.Print(0, [tf.reduce_mean(value_loss)], 'value loss: '),
tf.Print(0, [tf.reduce_mean(policy_loss)], 'policy loss: '))
with tf.control_dependencies([value_loss, policy_loss, print_losses]):
return summary[self._config.update_epochs // 2]
def _update_step(
self, observ, action, old_mean, old_logstd, reward, advantage, length):
"""Compute the current combined loss and perform a gradient update step.
Args:
observ: Sequences of observations.
action: Sequences of actions.
old_mean: Sequences of action means of the behavioral policy.
old_logstd: Sequences of action log stddevs of the behavioral policy.
reward: Sequences of reward.
advantage: Sequences of advantages.
length: Batch of sequence lengths.
Returns:
Tuple of value loss, policy loss, and summary tensor.
"""
value_loss, value_summary = self._value_loss(observ, reward, length)
network = self._network(observ, length)
policy_loss, policy_summary = self._policy_loss(
network.mean, network.logstd, old_mean, old_logstd, action,
advantage, length)
value_gradients, value_variables = (
zip(*self._optimizer.compute_gradients(value_loss)))
policy_gradients, policy_variables = (
zip(*self._optimizer.compute_gradients(policy_loss)))
all_gradients = value_gradients + policy_gradients
all_variables = value_variables + policy_variables
optimize = self._optimizer.apply_gradients(
zip(all_gradients, all_variables))
summary = tf.summary.merge([
value_summary, policy_summary,
tf.summary.scalar(
'value_gradient_norm', tf.global_norm(value_gradients)),
tf.summary.scalar(
'policy_gradient_norm', tf.global_norm(policy_gradients)),
utility.gradient_summaries(
zip(value_gradients, value_variables), dict(value=r'.*')),
utility.gradient_summaries(
zip(policy_gradients, policy_variables), dict(policy=r'.*'))])
with tf.control_dependencies([optimize]):
return [tf.identity(x) for x in (value_loss, policy_loss, summary)]
def _value_loss(self, observ, reward, length):
"""Compute the loss function for the value baseline.
The value loss is the difference between empirical and approximated returns
over the collected episodes. Returns the loss tensor and a summary strin.
Args:
observ: Sequences of observations.
reward: Sequences of reward.
length: Batch of sequence lengths.
Returns:
Tuple of loss tensor and summary tensor.
"""
with tf.name_scope('value_loss'):
value = self._network(observ, length).value
return_ = utility.discounted_return(
reward, length, self._config.discount)
advantage = return_ - value
value_loss = 0.5 * self._mask(advantage ** 2, length)
summary = tf.summary.merge([
tf.summary.histogram('value_loss', value_loss),
tf.summary.scalar('avg_value_loss', tf.reduce_mean(value_loss))])
value_loss = tf.reduce_mean(value_loss)
return tf.check_numerics(value_loss, 'value_loss'), summary
def _policy_loss(
self, mean, logstd, old_mean, old_logstd, action, advantage, length):
"""Compute the policy loss composed of multiple components.
1. The policy gradient loss is importance sampled from the data-collecting
policy at the beginning of training.
2. The second term is a KL penalty between the policy at the beginning of
training and the current policy.
3. Additionally, if this KL already changed more than twice the target
amount, we activate a strong penalty discouraging further divergence.
Args:
mean: Sequences of action means of the current policy.
logstd: Sequences of action log stddevs of the current policy.
old_mean: Sequences of action means of the behavioral policy.
old_logstd: Sequences of action log stddevs of the behavioral policy.
action: Sequences of actions.
advantage: Sequences of advantages.
length: Batch of sequence lengths.
Returns:
Tuple of loss tensor and summary tensor.
"""
with tf.name_scope('policy_loss'):
entropy = utility.diag_normal_entropy(mean, logstd)
kl = tf.reduce_mean(self._mask(utility.diag_normal_kl(
old_mean, old_logstd, mean, logstd), length), 1)
policy_gradient = tf.exp(
utility.diag_normal_logpdf(mean, logstd, action) -
utility.diag_normal_logpdf(old_mean, old_logstd, action))
surrogate_loss = -tf.reduce_mean(self._mask(
policy_gradient * tf.stop_gradient(advantage), length), 1)
kl_penalty = self._penalty * kl
cutoff_threshold = self._config.kl_target * self._config.kl_cutoff_factor
cutoff_count = tf.reduce_sum(
tf.cast(kl > cutoff_threshold, tf.int32))
with tf.control_dependencies([tf.cond(
cutoff_count > 0,
lambda: tf.Print(0, [cutoff_count], 'kl cutoff! '), int)]):
kl_cutoff = (
self._config.kl_cutoff_coef *
tf.cast(kl > cutoff_threshold, tf.float32) *
(kl - cutoff_threshold) ** 2)
policy_loss = surrogate_loss + kl_penalty + kl_cutoff
summary = tf.summary.merge([
tf.summary.histogram('entropy', entropy),
tf.summary.histogram('kl', kl),
tf.summary.histogram('surrogate_loss', surrogate_loss),
tf.summary.histogram('kl_penalty', kl_penalty),
tf.summary.histogram('kl_cutoff', kl_cutoff),
tf.summary.histogram('kl_penalty_combined', kl_penalty + kl_cutoff),
tf.summary.histogram('policy_loss', policy_loss),
tf.summary.scalar('avg_surr_loss', tf.reduce_mean(surrogate_loss)),
tf.summary.scalar('avg_kl_penalty', tf.reduce_mean(kl_penalty)),
tf.summary.scalar('avg_policy_loss', tf.reduce_mean(policy_loss))])
policy_loss = tf.reduce_mean(policy_loss, 0)
return tf.check_numerics(policy_loss, 'policy_loss'), summary
def _adjust_penalty(self, observ, old_mean, old_logstd, length):
"""Adjust the KL policy between the behavioral and current policy.
Compute how much the policy actually changed during the multiple
update steps. Adjust the penalty strength for the next training phase if we
overshot or undershot the target divergence too much.
Args:
observ: Sequences of observations.
old_mean: Sequences of action means of the behavioral policy.
old_logstd: Sequences of action log stddevs of the behavioral policy.
length: Batch of sequence lengths.
Returns:
Summary tensor.
"""
with tf.name_scope('adjust_penalty'):
network = self._network(observ, length)
assert_change = tf.assert_equal(
tf.reduce_all(tf.equal(network.mean, old_mean)), False,
message='policy should change')
print_penalty = tf.Print(0, [self._penalty], 'current penalty: ')
with tf.control_dependencies([assert_change, print_penalty]):
kl_change = tf.reduce_mean(self._mask(utility.diag_normal_kl(
old_mean, old_logstd, network.mean, network.logstd), length))
kl_change = tf.Print(kl_change, [kl_change], 'kl change: ')
maybe_increase = tf.cond(
kl_change > 1.3 * self._config.kl_target,
# pylint: disable=g-long-lambda
lambda: tf.Print(self._penalty.assign(
self._penalty * 1.5), [0], 'increase penalty '),
float)
maybe_decrease = tf.cond(
kl_change < 0.7 * self._config.kl_target,
# pylint: disable=g-long-lambda
lambda: tf.Print(self._penalty.assign(
self._penalty / 1.5), [0], 'decrease penalty '),
float)
with tf.control_dependencies([maybe_increase, maybe_decrease]):
return tf.summary.merge([
tf.summary.scalar('kl_change', kl_change),
tf.summary.scalar('penalty', self._penalty)])
def _mask(self, tensor, length):
"""Set padding elements of a batch of sequences to zero.
Useful to then safely sum along the time dimension.
Args:
tensor: Tensor of sequences.
length: Batch of sequence lengths.
Returns:
Masked sequences.
"""
with tf.name_scope('mask'):
range_ = tf.range(tensor.shape[1].value)
mask = tf.cast(range_[None, :] < length[:, None], tf.float32)
masked = tensor * mask
return tf.check_numerics(masked, 'masked')

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# Copyright 2017 The TensorFlow Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Memory that stores episodes."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
class EpisodeMemory(object):
"""Memory that stores episodes."""
def __init__(self, template, capacity, max_length, scope):
"""Create a memory that stores episodes.
Each transition tuple consists of quantities specified by the template.
These quantities would typically be be observartions, actions, rewards, and
done indicators.
Args:
template: List of tensors to derive shapes and dtypes of each transition.
capacity: Number of episodes, or rows, hold by the memory.
max_length: Allocated sequence length for the episodes.
scope: Variable scope to use for internal variables.
"""
self._capacity = capacity
self._max_length = max_length
with tf.variable_scope(scope) as var_scope:
self._scope = var_scope
self._length = tf.Variable(tf.zeros(capacity, tf.int32), False)
self._buffers = [
tf.Variable(tf.zeros(
[capacity, max_length] + elem.shape.as_list(),
elem.dtype), False)
for elem in template]
def length(self, rows=None):
"""Tensor holding the current length of episodes.
Args:
rows: Episodes to select length from, defaults to all.
Returns:
Batch tensor of sequence lengths.
"""
rows = tf.range(self._capacity) if rows is None else rows
return tf.gather(self._length, rows)
def append(self, transitions, rows=None):
"""Append a batch of transitions to rows of the memory.
Args:
transitions: Tuple of transition quantities with batch dimension.
rows: Episodes to append to, defaults to all.
Returns:
Operation.
"""
rows = tf.range(self._capacity) if rows is None else rows
assert rows.shape.ndims == 1
assert_capacity = tf.assert_less(
rows, self._capacity,
message='capacity exceeded')
with tf.control_dependencies([assert_capacity]):
assert_max_length = tf.assert_less(
tf.gather(self._length, rows), self._max_length,
message='max length exceeded')
append_ops = []
with tf.control_dependencies([assert_max_length]):
for buffer_, elements in zip(self._buffers, transitions):
timestep = tf.gather(self._length, rows)
indices = tf.stack([rows, timestep], 1)
append_ops.append(tf.scatter_nd_update(buffer_, indices, elements))
with tf.control_dependencies(append_ops):
episode_mask = tf.reduce_sum(tf.one_hot(
rows, self._capacity, dtype=tf.int32), 0)
return self._length.assign_add(episode_mask)
def replace(self, episodes, length, rows=None):
"""Replace full episodes.
Args:
episodes: Tuple of transition quantities with batch and time dimensions.
length: Batch of sequence lengths.
rows: Episodes to replace, defaults to all.
Returns:
Operation.
"""
rows = tf.range(self._capacity) if rows is None else rows
assert rows.shape.ndims == 1
assert_capacity = tf.assert_less(
rows, self._capacity, message='capacity exceeded')
with tf.control_dependencies([assert_capacity]):
assert_max_length = tf.assert_less_equal(
length, self._max_length, message='max length exceeded')
replace_ops = []
with tf.control_dependencies([assert_max_length]):
for buffer_, elements in zip(self._buffers, episodes):
replace_op = tf.scatter_update(buffer_, rows, elements)
replace_ops.append(replace_op)
with tf.control_dependencies(replace_ops):
return tf.scatter_update(self._length, rows, length)
def data(self, rows=None):
"""Access a batch of episodes from the memory.
Padding elements after the length of each episode are unspecified and might
contain old data.
Args:
rows: Episodes to select, defaults to all.
Returns:
Tuple containing a tuple of transition quantiries with batch and time
dimensions, and a batch of sequence lengths.
"""
rows = tf.range(self._capacity) if rows is None else rows
assert rows.shape.ndims == 1
episode = [tf.gather(buffer_, rows) for buffer_ in self._buffers]
length = tf.gather(self._length, rows)
return episode, length
def clear(self, rows=None):
"""Reset episodes in the memory.
Internally, this only sets their lengths to zero. The memory entries will
be overridden by future calls to append() or replace().
Args:
rows: Episodes to clear, defaults to all.
Returns:
Operation.
"""
rows = tf.range(self._capacity) if rows is None else rows
assert rows.shape.ndims == 1
return tf.scatter_update(self._length, rows, tf.zeros_like(rows))

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# Copyright 2017 The TensorFlow Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Normalize tensors based on streaming estimates of mean and variance."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
class StreamingNormalize(object):
"""Normalize tensors based on streaming estimates of mean and variance."""
def __init__(
self, template, center=True, scale=True, clip=10, name='normalize'):
"""Normalize tensors based on streaming estimates of mean and variance.
Centering the value, scaling it by the standard deviation, and clipping
outlier values are optional.
Args:
template: Example tensor providing shape and dtype of the vaule to track.
center: Python boolean indicating whether to subtract mean from values.
scale: Python boolean indicating whether to scale values by stddev.
clip: If and when to clip normalized values.
name: Parent scope of operations provided by this class.
"""
self._center = center
self._scale = scale
self._clip = clip
self._name = name
with tf.name_scope(name):
self._count = tf.Variable(0, False)
self._mean = tf.Variable(tf.zeros_like(template), False)
self._var_sum = tf.Variable(tf.zeros_like(template), False)
def transform(self, value):
"""Normalize a single or batch tensor.
Applies the activated transformations in the constructor using current
estimates of mean and variance.
Args:
value: Batch or single value tensor.
Returns:
Normalized batch or single value tensor.
"""
with tf.name_scope(self._name + '/transform'):
no_batch_dim = value.shape.ndims == self._mean.shape.ndims
if no_batch_dim:
# Add a batch dimension if necessary.
value = value[None, ...]
if self._center:
value -= self._mean[None, ...]
if self._scale:
# We cannot scale before seeing at least two samples.
value /= tf.cond(
self._count > 1, lambda: self._std() + 1e-8,
lambda: tf.ones_like(self._var_sum))[None]
if self._clip:
value = tf.clip_by_value(value, -self._clip, self._clip)
# Remove batch dimension if necessary.
if no_batch_dim:
value = value[0]
return tf.check_numerics(value, 'value')
def update(self, value):
"""Update the mean and variance estimates.
Args:
value: Batch or single value tensor.
Returns:
Summary tensor.
"""
with tf.name_scope(self._name + '/update'):
if value.shape.ndims == self._mean.shape.ndims:
# Add a batch dimension if necessary.
value = value[None, ...]
count = tf.shape(value)[0]
with tf.control_dependencies([self._count.assign_add(count)]):
step = tf.cast(self._count, tf.float32)
mean_delta = tf.reduce_sum(value - self._mean[None, ...], 0)
new_mean = self._mean + mean_delta / step
new_mean = tf.cond(self._count > 1, lambda: new_mean, lambda: value[0])
var_delta = (
value - self._mean[None, ...]) * (value - new_mean[None, ...])
new_var_sum = self._var_sum + tf.reduce_sum(var_delta, 0)
with tf.control_dependencies([new_mean, new_var_sum]):
update = self._mean.assign(new_mean), self._var_sum.assign(new_var_sum)
with tf.control_dependencies(update):
if value.shape.ndims == 1:
value = tf.reduce_mean(value)
return self._summary('value', tf.reduce_mean(value))
def reset(self):
"""Reset the estimates of mean and variance.
Resets the full state of this class.
Returns:
Operation.
"""
with tf.name_scope(self._name + '/reset'):
return tf.group(
self._count.assign(0),
self._mean.assign(tf.zeros_like(self._mean)),
self._var_sum.assign(tf.zeros_like(self._var_sum)))
def summary(self):
"""Summary string of mean and standard deviation.
Returns:
Summary tensor.
"""
with tf.name_scope(self._name + '/summary'):
mean_summary = tf.cond(
self._count > 0, lambda: self._summary('mean', self._mean), str)
std_summary = tf.cond(
self._count > 1, lambda: self._summary('stddev', self._std()), str)
return tf.summary.merge([mean_summary, std_summary])
def _std(self):
"""Computes the current estimate of the standard deviation.
Note that the standard deviation is not defined until at least two samples
were seen.
Returns:
Tensor of current variance.
"""
variance = tf.cond(
self._count > 1,
lambda: self._var_sum / tf.cast(self._count - 1, tf.float32),
lambda: tf.ones_like(self._var_sum) * float('nan'))
# The epsilon corrects for small negative variance values caused by
# the algorithm. It was empirically chosen to work with all environments
# tested.
return tf.sqrt(variance + 1e-4)
def _summary(self, name, tensor):
"""Create a scalar or histogram summary matching the rank of the tensor.
Args:
name: Name for the summary.
tensor: Tensor to summarize.
Returns:
Summary tensor.
"""
if tensor.shape.ndims == 0:
return tf.summary.scalar(name, tensor)
else:
return tf.summary.histogram(name, tensor)

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# Copyright 2017 The TensorFlow Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utilities for the PPO algorithm."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import math
import re
import tensorflow as tf
from tensorflow.python.client import device_lib
def reinit_nested_vars(variables, indices=None):
"""Reset all variables in a nested tuple to zeros.
Args:
variables: Nested tuple or list of variaables.
indices: Batch indices to reset, defaults to all.
Returns:
Operation.
"""
if isinstance(variables, (tuple, list)):
return tf.group(*[
reinit_nested_vars(variable, indices) for variable in variables])
if indices is None:
return variables.assign(tf.zeros_like(variables))
else:
zeros = tf.zeros([tf.shape(indices)[0]] + variables.shape[1:].as_list())
return tf.scatter_update(variables, indices, zeros)
def assign_nested_vars(variables, tensors, indices=None):
"""Assign tensors to matching nested tuple of variables.
Args:
variables: Nested tuple or list of variables to update.
tensors: Nested tuple or list of tensors to assign.
indices: Batch indices to assign to; default to all.
Returns:
Operation.
"""
if isinstance(variables, (tuple, list)):
return tf.group(*[
assign_nested_vars(variable, tensor)
for variable, tensor in zip(variables, tensors)])
if indices is None:
return variables.assign(tensors)
else:
return tf.scatter_update(variables, indices, tensors)
def discounted_return(reward, length, discount):
"""Discounted Monte-Carlo returns."""
timestep = tf.range(reward.shape[1].value)
mask = tf.cast(timestep[None, :] < length[:, None], tf.float32)
return_ = tf.reverse(tf.transpose(tf.scan(
lambda agg, cur: cur + discount * agg,
tf.transpose(tf.reverse(mask * reward, [1]), [1, 0]),
tf.zeros_like(reward[:, -1]), 1, False), [1, 0]), [1])
return tf.check_numerics(tf.stop_gradient(return_), 'return')
def fixed_step_return(reward, value, length, discount, window):
"""N-step discounted return."""
timestep = tf.range(reward.shape[1].value)
mask = tf.cast(timestep[None, :] < length[:, None], tf.float32)
return_ = tf.zeros_like(reward)
for _ in range(window):
return_ += reward
reward = discount * tf.concat(
[reward[:, 1:], tf.zeros_like(reward[:, -1:])], 1)
return_ += discount ** window * tf.concat(
[value[:, window:], tf.zeros_like(value[:, -window:]), 1])
return tf.check_numerics(tf.stop_gradient(mask * return_), 'return')
def lambda_return(reward, value, length, discount, lambda_):
"""TD-lambda returns."""
timestep = tf.range(reward.shape[1].value)
mask = tf.cast(timestep[None, :] < length[:, None], tf.float32)
sequence = mask * reward + discount * value * (1 - lambda_)
discount = mask * discount * lambda_
sequence = tf.stack([sequence, discount], 2)
return_ = tf.reverse(tf.transpose(tf.scan(
lambda agg, cur: cur[0] + cur[1] * agg,
tf.transpose(tf.reverse(sequence, [1]), [1, 2, 0]),
tf.zeros_like(value[:, -1]), 1, False), [1, 0]), [1])
return tf.check_numerics(tf.stop_gradient(return_), 'return')
def lambda_advantage(reward, value, length, discount):
"""Generalized Advantage Estimation."""
timestep = tf.range(reward.shape[1].value)
mask = tf.cast(timestep[None, :] < length[:, None], tf.float32)
next_value = tf.concat([value[:, 1:], tf.zeros_like(value[:, -1:])], 1)
delta = reward + discount * next_value - value
advantage = tf.reverse(tf.transpose(tf.scan(
lambda agg, cur: cur + discount * agg,
tf.transpose(tf.reverse(mask * delta, [1]), [1, 0]),
tf.zeros_like(delta[:, -1]), 1, False), [1, 0]), [1])
return tf.check_numerics(tf.stop_gradient(advantage), 'advantage')
def diag_normal_kl(mean0, logstd0, mean1, logstd1):
"""Epirical KL divergence of two normals with diagonal covariance."""
logstd0_2, logstd1_2 = 2 * logstd0, 2 * logstd1
return 0.5 * (
tf.reduce_sum(tf.exp(logstd0_2 - logstd1_2), -1) +
tf.reduce_sum((mean1 - mean0) ** 2 / tf.exp(logstd1_2), -1) +
tf.reduce_sum(logstd1_2, -1) - tf.reduce_sum(logstd0_2, -1) -
mean0.shape[-1].value)
def diag_normal_logpdf(mean, logstd, loc):
"""Log density of a normal with diagonal covariance."""
constant = -0.5 * math.log(2 * math.pi) - logstd
value = -0.5 * ((loc - mean) / tf.exp(logstd)) ** 2
return tf.reduce_sum(constant + value, -1)
def diag_normal_entropy(mean, logstd):
"""Empirical entropy of a normal with diagonal covariance."""
constant = mean.shape[-1].value * math.log(2 * math.pi * math.e)
return (constant + tf.reduce_sum(2 * logstd, 1)) / 2
def available_gpus():
"""List of GPU device names detected by TensorFlow."""
local_device_protos = device_lib.list_local_devices()
return [x.name for x in local_device_protos if x.device_type == 'GPU']
def gradient_summaries(grad_vars, groups=None, scope='gradients'):
"""Create histogram summaries of the gradient.
Summaries can be grouped via regexes matching variables names.
Args:
grad_vars: List of (gradient, variable) tuples as returned by optimizers.
groups: Mapping of name to regex for grouping summaries.
scope: Name scope for this operation.
Returns:
Summary tensor.
"""
groups = groups or {r'all': r'.*'}
grouped = collections.defaultdict(list)
for grad, var in grad_vars:
if grad is None:
continue
for name, pattern in groups.items():
if re.match(pattern, var.name):
name = re.sub(pattern, name, var.name)
grouped[name].append(grad)
for name in groups:
if name not in grouped:
tf.logging.warn("No variables matching '{}' group.".format(name))
summaries = []
for name, grads in grouped.items():
grads = [tf.reshape(grad, [-1]) for grad in grads]
grads = tf.concat(grads, 0)
summaries.append(tf.summary.histogram(scope + '/' + name, grads))
return tf.summary.merge(summaries)
def variable_summaries(vars_, groups=None, scope='weights'):
"""Create histogram summaries for the provided variables.
Summaries can be grouped via regexes matching variables names.
Args:
vars_: List of variables to summarize.
groups: Mapping of name to regex for grouping summaries.
scope: Name scope for this operation.
Returns:
Summary tensor.
"""
groups = groups or {r'all': r'.*'}
grouped = collections.defaultdict(list)
for var in vars_:
for name, pattern in groups.items():
if re.match(pattern, var.name):
name = re.sub(pattern, name, var.name)
grouped[name].append(var)
for name in groups:
if name not in grouped:
tf.logging.warn("No variables matching '{}' group.".format(name))
summaries = []
# pylint: disable=redefined-argument-from-local
for name, vars_ in grouped.items():
vars_ = [tf.reshape(var, [-1]) for var in vars_]
vars_ = tf.concat(vars_, 0)
summaries.append(tf.summary.histogram(scope + '/' + name, vars_))
return tf.summary.merge(summaries)

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# Copyright 2017 The TensorFlow Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tools for reinforcement learning."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from . import wrappers
from .attr_dict import AttrDict
from .batch_env import BatchEnv
from .count_weights import count_weights
from .in_graph_batch_env import InGraphBatchEnv
from .in_graph_env import InGraphEnv
from .loop import Loop
from .mock_algorithm import MockAlgorithm
from .mock_environment import MockEnvironment
from .simulate import simulate
from .streaming_mean import StreamingMean

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# Copyright 2017 The TensorFlow Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Wrap a dictionary to access keys as attributes."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import contextlib
class AttrDict(dict):
"""Wrap a dictionary to access keys as attributes."""
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
super(AttrDict, self).__setattr__('_mutable', False)
def __getattr__(self, key):
# Do not provide None for unimplemented magic attributes.
if key.startswith('__'):
raise AttributeError
return self.get(key, None)
def __setattr__(self, key, value):
if not self._mutable:
message = "Cannot set attribute '{}'.".format(key)
message += " Use 'with obj.unlocked:' scope to set attributes."
raise RuntimeError(message)
if key.startswith('__'):
raise AttributeError("Cannot set magic attribute '{}'".format(key))
self[key] = value
@property
@contextlib.contextmanager
def unlocked(self):
super(AttrDict, self).__setattr__('_mutable', True)
yield
super(AttrDict, self).__setattr__('_mutable', False)
def copy(self):
return type(self)(super(AttrDict, self).copy())

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# Copyright 2017 The TensorFlow Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Combine multiple environments to step them in batch."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
class BatchEnv(object):
"""Combine multiple environments to step them in batch."""
def __init__(self, envs, blocking):
"""Combine multiple environments to step them in batch.
To step environments in parallel, environments must support a
`blocking=False` argument to their step and reset functions that makes them
return callables instead to receive the result at a later time.
Args:
envs: List of environments.
blocking: Step environments after another rather than in parallel.
Raises:
ValueError: Environments have different observation or action spaces.
"""
self._envs = envs
self._blocking = blocking
observ_space = self._envs[0].observation_space
if not all(env.observation_space == observ_space for env in self._envs):
raise ValueError('All environments must use the same observation space.')
action_space = self._envs[0].action_space
if not all(env.action_space == action_space for env in self._envs):
raise ValueError('All environments must use the same observation space.')
def __len__(self):
"""Number of combined environments."""
return len(self._envs)
def __getitem__(self, index):
"""Access an underlying environment by index."""
return self._envs[index]
def __getattr__(self, name):
"""Forward unimplemented attributes to one of the original environments.
Args:
name: Attribute that was accessed.
Returns:
Value behind the attribute name one of the wrapped environments.
"""
return getattr(self._envs[0], name)
def step(self, actions):
"""Forward a batch of actions to the wrapped environments.
Args:
actions: Batched action to apply to the environment.
Raises:
ValueError: Invalid actions.
Returns:
Batch of observations, rewards, and done flags.
"""
for index, (env, action) in enumerate(zip(self._envs, actions)):
if not env.action_space.contains(action):
message = 'Invalid action at index {}: {}'
raise ValueError(message.format(index, action))
if self._blocking:
transitions = [
env.step(action)
for env, action in zip(self._envs, actions)]
else:
transitions = [
env.step(action, blocking=False)
for env, action in zip(self._envs, actions)]
transitions = [transition() for transition in transitions]
observs, rewards, dones, infos = zip(*transitions)
observ = np.stack(observs)
reward = np.stack(rewards)
done = np.stack(dones)
info = tuple(infos)
return observ, reward, done, info
def reset(self, indices=None):
"""Reset the environment and convert the resulting observation.
Args:
indices: The batch indices of environments to reset; defaults to all.
Returns:
Batch of observations.
"""
if indices is None:
indices = np.arange(len(self._envs))
if self._blocking:
observs = [self._envs[index].reset() for index in indices]
else:
observs = [self._envs[index].reset(blocking=False) for index in indices]
observs = [observ() for observ in observs]
observ = np.stack(observs)
return observ
def close(self):
"""Send close messages to the external process and join them."""
for env in self._envs:
if hasattr(env, 'close'):
env.close()

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# Copyright 2017 The TensorFlow Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Count learnable parameters."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
import numpy as np
import tensorflow as tf
def count_weights(scope=None, exclude=None, graph=None):
"""Count learnable parameters.
Args:
scope: Resrict the count to a variable scope.
exclude: Regex to match variable names to exclude.
graph: Operate on a graph other than the current default graph.
Returns:
Number of learnable parameters as integer.
"""
if scope:
scope = scope if scope.endswith('/') else scope + '/'
graph = graph or tf.get_default_graph()
vars_ = graph.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
if scope:
vars_ = [var for var in vars_ if var.name.startswith(scope)]
if exclude:
exclude = re.compile(exclude)
vars_ = [var for var in vars_ if not exclude.match(var.name)]
shapes = [var.get_shape().as_list() for var in vars_]
return int(sum(np.prod(shape) for shape in shapes))

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# Copyright 2017 The TensorFlow Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Batch of environments inside the TensorFlow graph."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gym
import tensorflow as tf
class InGraphBatchEnv(object):
"""Batch of environments inside the TensorFlow graph.
The batch of environments will be stepped and reset inside of the graph using
a tf.py_func(). The current batch of observations, actions, rewards, and done
flags are held in according variables.
"""
def __init__(self, batch_env):
"""Batch of environments inside the TensorFlow graph.
Args:
batch_env: Batch environment.
"""
self._batch_env = batch_env
observ_shape = self._parse_shape(self._batch_env.observation_space)
observ_dtype = self._parse_dtype(self._batch_env.observation_space)
action_shape = self._parse_shape(self._batch_env.action_space)
action_dtype = self._parse_dtype(self._batch_env.action_space)
with tf.variable_scope('env_temporary'):
self._observ = tf.Variable(
tf.zeros((len(self._batch_env),) + observ_shape, observ_dtype),
name='observ', trainable=False)
self._action = tf.Variable(
tf.zeros((len(self._batch_env),) + action_shape, action_dtype),
name='action', trainable=False)
self._reward = tf.Variable(
tf.zeros((len(self._batch_env),), tf.float32),
name='reward', trainable=False)
self._done = tf.Variable(
tf.cast(tf.ones((len(self._batch_env),)), tf.bool),
name='done', trainable=False)
def __getattr__(self, name):
"""Forward unimplemented attributes to one of the original environments.
Args:
name: Attribute that was accessed.
Returns:
Value behind the attribute name in one of the original environments.
"""
return getattr(self._batch_env, name)
def __len__(self):
"""Number of combined environments."""
return len(self._batch_env)
def __getitem__(self, index):
"""Access an underlying environment by index."""
return self._batch_env[index]
def simulate(self, action):
"""Step the batch of environments.
The results of the step can be accessed from the variables defined below.
Args:
action: Tensor holding the batch of actions to apply.
Returns:
Operation.
"""
with tf.name_scope('environment/simulate'):
if action.dtype in (tf.float16, tf.float32, tf.float64):
action = tf.check_numerics(action, 'action')
observ_dtype = self._parse_dtype(self._batch_env.observation_space)
observ, reward, done = tf.py_func(
lambda a: self._batch_env.step(a)[:3], [action],
[observ_dtype, tf.float32, tf.bool], name='step')
observ = tf.check_numerics(observ, 'observ')
reward = tf.check_numerics(reward, 'reward')
return tf.group(
self._observ.assign(observ),
self._action.assign(action),
self._reward.assign(reward),
self._done.assign(done))
def reset(self, indices=None):
"""Reset the batch of environments.
Args:
indices: The batch indices of the environments to reset; defaults to all.
Returns:
Batch tensor of the new observations.
"""
if indices is None:
indices = tf.range(len(self._batch_env))
observ_dtype = self._parse_dtype(self._batch_env.observation_space)
observ = tf.py_func(
self._batch_env.reset, [indices], observ_dtype, name='reset')
observ = tf.check_numerics(observ, 'observ')
reward = tf.zeros_like(indices, tf.float32)
done = tf.zeros_like(indices, tf.bool)
with tf.control_dependencies([
tf.scatter_update(self._observ, indices, observ),
tf.scatter_update(self._reward, indices, reward),
tf.scatter_update(self._done, indices, done)]):
return tf.identity(observ)
@property
def observ(self):
"""Access the variable holding the current observation."""
return self._observ
@property
def action(self):
"""Access the variable holding the last recieved action."""
return self._action
@property
def reward(self):
"""Access the variable holding the current reward."""
return self._reward
@property
def done(self):
"""Access the variable indicating whether the episode is done."""
return self._done
def close(self):
"""Send close messages to the external process and join them."""
self._batch_env.close()
def _parse_shape(self, space):
"""Get a tensor shape from a OpenAI Gym space.
Args:
space: Gym space.
Returns:
Shape tuple.
"""
if isinstance(space, gym.spaces.Discrete):
return ()
if isinstance(space, gym.spaces.Box):
return space.shape
raise NotImplementedError()
def _parse_dtype(self, space):
"""Get a tensor dtype from a OpenAI Gym space.
Args:
space: Gym space.
Returns:
TensorFlow data type.
"""
if isinstance(space, gym.spaces.Discrete):
return tf.int32
if isinstance(space, gym.spaces.Box):
return tf.float32
raise NotImplementedError()

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# Copyright 2017 The TensorFlow Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Put an OpenAI Gym environment into the TensorFlow graph."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gym
import tensorflow as tf
class InGraphEnv(object):
"""Put an OpenAI Gym environment into the TensorFlow graph.
The environment will be stepped and reset inside of the graph using
tf.py_func(). The current observation, action, reward, and done flag are held
in according variables.
"""
def __init__(self, env):
"""Put an OpenAI Gym environment into the TensorFlow graph.
Args:
env: OpenAI Gym environment.
"""
self._env = env
observ_shape = self._parse_shape(self._env.observation_space)
observ_dtype = self._parse_dtype(self._env.observation_space)
action_shape = self._parse_shape(self._env.action_space)
action_dtype = self._parse_dtype(self._env.action_space)
with tf.name_scope('environment'):
self._observ = tf.Variable(
tf.zeros(observ_shape, observ_dtype), name='observ', trainable=False)
self._action = tf.Variable(
tf.zeros(action_shape, action_dtype), name='action', trainable=False)
self._reward = tf.Variable(
0.0, dtype=tf.float32, name='reward', trainable=False)
self._done = tf.Variable(
True, dtype=tf.bool, name='done', trainable=False)
self._step = tf.Variable(
0, dtype=tf.int32, name='step', trainable=False)
def __getattr__(self, name):
"""Forward unimplemented attributes to the original environment.
Args:
name: Attribute that was accessed.
Returns:
Value behind the attribute name in the wrapped environment.
"""
return getattr(self._env, name)
def simulate(self, action):
"""Step the environment.
The result of the step can be accessed from the variables defined below.
Args:
action: Tensor holding the action to apply.
Returns:
Operation.
"""
with tf.name_scope('environment/simulate'):
if action.dtype in (tf.float16, tf.float32, tf.float64):
action = tf.check_numerics(action, 'action')
observ_dtype = self._parse_dtype(self._env.observation_space)
observ, reward, done = tf.py_func(
lambda a: self._env.step(a)[:3], [action],
[observ_dtype, tf.float32, tf.bool], name='step')
observ = tf.check_numerics(observ, 'observ')
reward = tf.check_numerics(reward, 'reward')
return tf.group(
self._observ.assign(observ),
self._action.assign(action),
self._reward.assign(reward),
self._done.assign(done),
self._step.assign_add(1))
def reset(self):
"""Reset the environment.
Returns:
Tensor of the current observation.
"""
observ_dtype = self._parse_dtype(self._env.observation_space)
observ = tf.py_func(self._env.reset, [], observ_dtype, name='reset')
observ = tf.check_numerics(observ, 'observ')
with tf.control_dependencies([
self._observ.assign(observ),
self._reward.assign(0),
self._done.assign(False)]):
return tf.identity(observ)
@property
def observ(self):
"""Access the variable holding the current observation."""
return self._observ
@property
def action(self):
"""Access the variable holding the last recieved action."""
return self._action
@property
def reward(self):
"""Access the variable holding the current reward."""
return self._reward
@property
def done(self):
"""Access the variable indicating whether the episode is done."""
return self._done
@property
def step(self):
"""Access the variable containg total steps of this environment."""
return self._step
def _parse_shape(self, space):
"""Get a tensor shape from a OpenAI Gym space.
Args:
space: Gym space.
Returns:
Shape tuple.
"""
if isinstance(space, gym.spaces.Discrete):
return ()
if isinstance(space, gym.spaces.Box):
return space.shape
raise NotImplementedError()
def _parse_dtype(self, space):
"""Get a tensor dtype from a OpenAI Gym space.
Args:
space: Gym space.
Returns:
TensorFlow data type.
"""
if isinstance(space, gym.spaces.Discrete):
return tf.int32
if isinstance(space, gym.spaces.Box):
return tf.float32
raise NotImplementedError()

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# Copyright 2017 The TensorFlow Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Execute operations in a loop and coordinate logging and checkpoints."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import os
import tensorflow as tf
from . import streaming_mean
_Phase = collections.namedtuple(
'Phase',
'name, writer, op, batch, steps, feed, report_every, log_every,'
'checkpoint_every')
class Loop(object):
"""Execute operations in a loop and coordinate logging and checkpoints.
Supports multiple phases, that define their own operations to run, and
intervals for reporting scores, logging summaries, and storing checkpoints.
All class state is stored in-graph to properly recover from checkpoints.
"""
def __init__(self, logdir, step=None, log=None, report=None, reset=None):
"""Execute operations in a loop and coordinate logging and checkpoints.
The step, log, report, and report arguments will get created if not
provided. Reset is used to indicate switching to a new phase, so that the
model can start a new computation in case its computation is split over
multiple training steps.
Args:
logdir: Will contain checkpoints and summaries for each phase.
step: Variable of the global step (optional).
log: Tensor indicating to the model to compute summary tensors.
report: Tensor indicating to the loop to report the current mean score.
reset: Tensor indicating to the model to start a new computation.
"""
self._logdir = logdir
self._step = (
tf.Variable(0, False, name='global_step') if step is None else step)
self._log = tf.placeholder(tf.bool) if log is None else log
self._report = tf.placeholder(tf.bool) if report is None else report
self._reset = tf.placeholder(tf.bool) if reset is None else reset
self._phases = []
def add_phase(
self, name, done, score, summary, steps,
report_every=None, log_every=None, checkpoint_every=None, feed=None):
"""Add a phase to the loop protocol.
If the model breaks long computation into multiple steps, the done tensor
indicates whether the current score should be added to the mean counter.
For example, in reinforcement learning we only have a valid score at the
end of the episode.
Score and done tensors can either be scalars or vectors, to support
single and batched computations.
Args:
name: Name for the phase, used for the summary writer.
done: Tensor indicating whether current score can be used.
score: Tensor holding the current, possibly intermediate, score.
summary: Tensor holding summary string to write if not an empty string.
steps: Duration of the phase in steps.
report_every: Yield mean score every this number of steps.
log_every: Request summaries via `log` tensor every this number of steps.
checkpoint_every: Write checkpoint every this number of steps.
feed: Additional feed dictionary for the session run call.
Raises:
ValueError: Unknown rank for done or score tensors.
"""
done = tf.convert_to_tensor(done, tf.bool)
score = tf.convert_to_tensor(score, tf.float32)
summary = tf.convert_to_tensor(summary, tf.string)
feed = feed or {}
if done.shape.ndims is None or score.shape.ndims is None:
raise ValueError("Rank of 'done' and 'score' tensors must be known.")
writer = self._logdir and tf.summary.FileWriter(
os.path.join(self._logdir, name), tf.get_default_graph(),
flush_secs=60)
op = self._define_step(done, score, summary)
batch = 1 if score.shape.ndims == 0 else score.shape[0].value
self._phases.append(_Phase(
name, writer, op, batch, int(steps), feed, report_every,
log_every, checkpoint_every))
def run(self, sess, saver, max_step=None):
"""Run the loop schedule for a specified number of steps.
Call the operation of the current phase until the global step reaches the
specified maximum step. Phases are repeated over and over in the order they
were added.
Args:
sess: Session to use to run the phase operation.
saver: Saver used for checkpointing.
max_step: Run the operations until the step reaches this limit.
Yields:
Reported mean scores.
"""
global_step = sess.run(self._step)
steps_made = 1
while True:
if max_step and global_step >= max_step:
break
phase, epoch, steps_in = self._find_current_phase(global_step)
phase_step = epoch * phase.steps + steps_in
if steps_in % phase.steps < steps_made:
message = '\n' + ('-' * 50) + '\n'
message += 'Phase {} (phase step {}, global step {}).'
tf.logging.info(message.format(phase.name, phase_step, global_step))
# Populate book keeping tensors.
phase.feed[self._reset] = (steps_in < steps_made)
phase.feed[self._log] = (
phase.writer and
self._is_every_steps(phase_step, phase.batch, phase.log_every))
phase.feed[self._report] = (
self._is_every_steps(phase_step, phase.batch, phase.report_every))
summary, mean_score, global_step, steps_made = sess.run(
phase.op, phase.feed)
if self._is_every_steps(phase_step, phase.batch, phase.checkpoint_every):
self._store_checkpoint(sess, saver, global_step)
if self._is_every_steps(phase_step, phase.batch, phase.report_every):
yield mean_score
if summary and phase.writer:
# We want smaller phases to catch up at the beginnig of each epoch so
# that their graphs are aligned.
longest_phase = max(phase.steps for phase in self._phases)
summary_step = epoch * longest_phase + steps_in
phase.writer.add_summary(summary, summary_step)
def _is_every_steps(self, phase_step, batch, every):
"""Determine whether a periodic event should happen at this step.
Args:
phase_step: The incrementing step.
batch: The number of steps progressed at once.
every: The interval of the periode.
Returns:
Boolean of whether the event should happen.
"""
if not every:
return False
covered_steps = range(phase_step, phase_step + batch)
return any((step + 1) % every == 0 for step in covered_steps)
def _find_current_phase(self, global_step):
"""Determine the current phase based on the global step.
This ensures continuing the correct phase after restoring checkoints.
Args:
global_step: The global number of steps performed across all phases.
Returns:
Tuple of phase object, epoch number, and phase steps within the epoch.
"""
epoch_size = sum(phase.steps for phase in self._phases)
epoch = int(global_step // epoch_size)
steps_in = global_step % epoch_size
for phase in self._phases:
if steps_in < phase.steps:
return phase, epoch, steps_in
steps_in -= phase.steps
def _define_step(self, done, score, summary):
"""Combine operations of a phase.
Keeps track of the mean score and when to report it.
Args:
done: Tensor indicating whether current score can be used.
score: Tensor holding the current, possibly intermediate, score.
summary: Tensor holding summary string to write if not an empty string.
Returns:
Tuple of summary tensor, mean score, and new global step. The mean score
is zero for non reporting steps.
"""
if done.shape.ndims == 0:
done = done[None]
if score.shape.ndims == 0:
score = score[None]
score_mean = streaming_mean.StreamingMean((), tf.float32)
with tf.control_dependencies([done, score, summary]):
done_score = tf.gather(score, tf.where(done)[:, 0])
submit_score = tf.cond(
tf.reduce_any(done), lambda: score_mean.submit(done_score), tf.no_op)
with tf.control_dependencies([submit_score]):
mean_score = tf.cond(self._report, score_mean.clear, float)
steps_made = tf.shape(score)[0]
next_step = self._step.assign_add(steps_made)
with tf.control_dependencies([mean_score, next_step]):
return tf.identity(summary), mean_score, next_step, steps_made
def _store_checkpoint(self, sess, saver, global_step):
"""Store a checkpoint if a log directory was provided to the constructor.
The directory will be created if needed.
Args:
sess: Session containing variables to store.
saver: Saver used for checkpointing.
global_step: Step number of the checkpoint name.
"""
if not self._logdir or not saver:
return
tf.gfile.MakeDirs(self._logdir)
filename = os.path.join(self._logdir, 'model.ckpt')
saver.save(sess, filename, global_step)

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# Copyright 2017 The TensorFlow Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Mock algorithm for testing reinforcement learning code."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
class MockAlgorithm(object):
"""Produce random actions and empty summaries."""
def __init__(self, envs):
"""Produce random actions and empty summaries.
Args:
envs: List of in-graph environments.
"""
self._envs = envs
def begin_episode(self, unused_agent_indices):
return tf.constant('')
def perform(self, agent_indices, unused_observ):
shape = (tf.shape(agent_indices)[0],) + self._envs[0].action_space.shape
low = self._envs[0].action_space.low
high = self._envs[0].action_space.high
action = tf.random_uniform(shape) * (high - low) + low
return action, tf.constant('')
def experience(self, unused_agent_indices, *unused_transition):
return tf.constant('')
def end_episode(self, unused_agent_indices):
return tf.constant('')

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# Copyright 2017 The TensorFlow Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Mock environment for testing reinforcement learning code."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gym
import gym.spaces
import numpy as np
class MockEnvironment(object):
"""Generate random agent input and keep track of statistics."""
def __init__(self, observ_shape, action_shape, min_duration, max_duration):
"""Generate random agent input and keep track of statistics.
Args:
observ_shape: Shape for the random observations.
action_shape: Shape for the action space.
min_duration: Minimum number of steps per episode.
max_duration: Maximum number of steps per episode.
Attributes:
steps: List of actual simulated lengths for all episodes.
durations: List of decided lengths for all episodes.
"""
self._observ_shape = observ_shape
self._action_shape = action_shape
self._min_duration = min_duration
self._max_duration = max_duration
self._random = np.random.RandomState(0)
self.steps = []
self.durations = []
@property
def observation_space(self):
low = np.zeros(self._observ_shape)
high = np.ones(self._observ_shape)
return gym.spaces.Box(low, high)
@property
def action_space(self):
low = np.zeros(self._action_shape)
high = np.ones(self._action_shape)
return gym.spaces.Box(low, high)
@property
def unwrapped(self):
return self
def step(self, action):
assert self.action_space.contains(action)
assert self.steps[-1] < self.durations[-1]
self.steps[-1] += 1
observ = self._current_observation()
reward = self._current_reward()
done = self.steps[-1] >= self.durations[-1]
info = {}
return observ, reward, done, info
def reset(self):
duration = self._random.randint(self._min_duration, self._max_duration + 1)
self.steps.append(0)
self.durations.append(duration)
return self._current_observation()
def _current_observation(self):
return self._random.uniform(0, 1, self._observ_shape)
def _current_reward(self):
return self._random.uniform(-1, 1)

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# Copyright 2017 The TensorFlow Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""In-graph simulation step of a vectorized algorithm with environments."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from . import streaming_mean
def simulate(batch_env, algo, log=True, reset=False):
"""Simulation step of a vecrotized algorithm with in-graph environments.
Integrates the operations implemented by the algorithm and the environments
into a combined operation.
Args:
batch_env: In-graph batch environment.
algo: Algorithm instance implementing required operations.
log: Tensor indicating whether to compute and return summaries.
reset: Tensor causing all environments to reset.
Returns:
Tuple of tensors containing done flags for the current episodes, possibly
intermediate scores for the episodes, and a summary tensor.
"""
def _define_begin_episode(agent_indices):
"""Reset environments, intermediate scores and durations for new episodes.
Args:
agent_indices: Tensor containing batch indices starting an episode.
Returns:
Summary tensor.
"""
assert agent_indices.shape.ndims == 1
zero_scores = tf.zeros_like(agent_indices, tf.float32)
zero_durations = tf.zeros_like(agent_indices)
reset_ops = [
batch_env.reset(agent_indices),
tf.scatter_update(score, agent_indices, zero_scores),
tf.scatter_update(length, agent_indices, zero_durations)]
with tf.control_dependencies(reset_ops):
return algo.begin_episode(agent_indices)
def _define_step():
"""Request actions from the algorithm and apply them to the environments.
Increments the lengths of all episodes and increases their scores by the
current reward. After stepping the environments, provides the full
transition tuple to the algorithm.
Returns:
Summary tensor.
"""
prevob = batch_env.observ + 0 # Ensure a copy of the variable value.
agent_indices = tf.range(len(batch_env))
action, step_summary = algo.perform(agent_indices, prevob)
action.set_shape(batch_env.action.shape)
with tf.control_dependencies([batch_env.simulate(action)]):
add_score = score.assign_add(batch_env.reward)
inc_length = length.assign_add(tf.ones(len(batch_env), tf.int32))
with tf.control_dependencies([add_score, inc_length]):
agent_indices = tf.range(len(batch_env))
experience_summary = algo.experience(
agent_indices, prevob, batch_env.action, batch_env.reward,
batch_env.done, batch_env.observ)
return tf.summary.merge([step_summary, experience_summary])
def _define_end_episode(agent_indices):
"""Notify the algorithm of ending episodes.
Also updates the mean score and length counters used for summaries.
Args:
agent_indices: Tensor holding batch indices that end their episodes.
Returns:
Summary tensor.
"""
assert agent_indices.shape.ndims == 1
submit_score = mean_score.submit(tf.gather(score, agent_indices))
submit_length = mean_length.submit(
tf.cast(tf.gather(length, agent_indices), tf.float32))
with tf.control_dependencies([submit_score, submit_length]):
return algo.end_episode(agent_indices)
def _define_summaries():
"""Reset the average score and duration, and return them as summary.
Returns:
Summary string.
"""
score_summary = tf.cond(
tf.logical_and(log, tf.cast(mean_score.count, tf.bool)),
lambda: tf.summary.scalar('mean_score', mean_score.clear()), str)
length_summary = tf.cond(
tf.logical_and(log, tf.cast(mean_length.count, tf.bool)),
lambda: tf.summary.scalar('mean_length', mean_length.clear()), str)
return tf.summary.merge([score_summary, length_summary])
with tf.name_scope('simulate'):
log = tf.convert_to_tensor(log)
reset = tf.convert_to_tensor(reset)
with tf.variable_scope('simulate_temporary'):
score = tf.Variable(
tf.zeros(len(batch_env), dtype=tf.float32), False, name='score')
length = tf.Variable(
tf.zeros(len(batch_env), dtype=tf.int32), False, name='length')
mean_score = streaming_mean.StreamingMean((), tf.float32)
mean_length = streaming_mean.StreamingMean((), tf.float32)
agent_indices = tf.cond(
reset,
lambda: tf.range(len(batch_env)),
lambda: tf.cast(tf.where(batch_env.done)[:, 0], tf.int32))
begin_episode = tf.cond(
tf.cast(tf.shape(agent_indices)[0], tf.bool),
lambda: _define_begin_episode(agent_indices), str)
with tf.control_dependencies([begin_episode]):
step = _define_step()
with tf.control_dependencies([step]):
agent_indices = tf.cast(tf.where(batch_env.done)[:, 0], tf.int32)
end_episode = tf.cond(
tf.cast(tf.shape(agent_indices)[0], tf.bool),
lambda: _define_end_episode(agent_indices), str)
with tf.control_dependencies([end_episode]):
summary = tf.summary.merge([
_define_summaries(), begin_episode, step, end_episode])
with tf.control_dependencies([summary]):
done, score = tf.identity(batch_env.done), tf.identity(score)
return done, score, summary

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examples/pybullet/gym/pybullet_envs/agents/tools/streaming_mean.py Normal file
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@ -0,0 +1,67 @@
# Copyright 2017 The TensorFlow Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Compute a streaming estimation of the mean of submitted tensors."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
class StreamingMean(object):
"""Compute a streaming estimation of the mean of submitted tensors."""
def __init__(self, shape, dtype):
"""Specify the shape and dtype of the mean to be estimated.
Note that a float mean to zero submitted elements is NaN, while computing
the integer mean of zero elements raises a division by zero error.
Args:
shape: Shape of the mean to compute.
dtype: Data type of the mean to compute.
"""
self._dtype = dtype
self._sum = tf.Variable(lambda: tf.zeros(shape, dtype), False)
self._count = tf.Variable(lambda: 0, trainable=False)
@property
def value(self):
"""The current value of the mean."""
return self._sum / tf.cast(self._count, self._dtype)
@property
def count(self):
"""The number of submitted samples."""
return self._count
def submit(self, value):
"""Submit a single or batch tensor to refine the streaming mean."""
# Add a batch dimension if necessary.
if value.shape.ndims == self._sum.shape.ndims:
value = value[None, ...]
return tf.group(
self._sum.assign_add(tf.reduce_sum(value, 0)),
self._count.assign_add(tf.shape(value)[0]))
def clear(self):
"""Return the mean estimate and reset the streaming statistics."""
value = self._sum / tf.cast(self._count, self._dtype)
with tf.control_dependencies([value]):
reset_value = self._sum.assign(tf.zeros_like(self._sum))
reset_count = self._count.assign(0)
with tf.control_dependencies([reset_value, reset_count]):
return tf.identity(value)

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# Copyright 2017 The TensorFlow Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Wrappers for OpenAI Gym environments."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import atexit
import multiprocessing
import sys
import traceback
import gym
import gym.spaces
import numpy as np
import tensorflow as tf
class AutoReset(object):
"""Automatically reset environment when the episode is done."""
def __init__(self, env):
self._env = env
self._done = True
def __getattr__(self, name):
return getattr(self._env, name)
def step(self, action):
if self._done:
observ, reward, done, info = self._env.reset(), 0.0, False, {}
else:
observ, reward, done, info = self._env.step(action)
self._done = done
return observ, reward, done, info
def reset(self):
self._done = False
return self._env.reset()
class ActionRepeat(object):
"""Repeat the agent action multiple steps."""
def __init__(self, env, amount):
self._env = env
self._amount = amount
def __getattr__(self, name):
return getattr(self._env, name)
def step(self, action):
done = False
total_reward = 0
current_step = 0
while current_step < self._amount and not done:
observ, reward, done, info = self._env.step(action)
total_reward += reward
current_step += 1
return observ, total_reward, done, info
class RandomStart(object):
"""Perform random number of random actions at the start of the episode."""
def __init__(self, env, max_steps):
self._env = env
self._max_steps = max_steps
def __getattr__(self, name):
return getattr(self._env, name)
def reset(self):
observ = self._env.reset()
random_steps = np.random.randint(0, self._max_steps)
for _ in range(random_steps):
action = self._env.action_space.sample()
observ, unused_reward, done, unused_info = self._env.step(action)
if done:
tf.logging.warning('Episode ended during random start.')
return self.reset()
return observ
class FrameHistory(object):
"""Augment the observation with past observations."""
def __init__(self, env, past_indices, flatten):
"""Augment the observation with past observations.
Implemented as a Numpy ring buffer holding the necessary past observations.
Args:
env: OpenAI Gym environment to wrap.
past_indices: List of non-negative integers indicating the time offsets
from the current time step of observations to include.
flatten: Concatenate the past observations rather than stacking them.
Raises:
KeyError: The current observation is not included in the indices.
"""
if 0 not in past_indices:
raise KeyError('Past indices should include 0 for the current frame.')
self._env = env
self._past_indices = past_indices
self._step = 0
self._buffer = None
self._capacity = max(past_indices)
self._flatten = flatten
def __getattr__(self, name):
return getattr(self._env, name)
@property
def observation_space(self):
low = self._env.observation_space.low
high = self._env.observation_space.high
low = np.repeat(low[None, ...], len(self._past_indices), 0)
high = np.repeat(high[None, ...], len(self._past_indices), 0)
if self._flatten:
low = np.reshape(low, (-1,) + low.shape[2:])
high = np.reshape(high, (-1,) + high.shape[2:])
return gym.spaces.Box(low, high)
def step(self, action):
observ, reward, done, info = self._env.step(action)
self._step += 1
self._buffer[self._step % self._capacity] = observ
observ = self._select_frames()
return observ, reward, done, info
def reset(self):
observ = self._env.reset()
self._buffer = np.repeat(observ[None, ...], self._capacity, 0)
self._step = 0
return self._select_frames()
def _select_frames(self):
indices = [
(self._step - index) % self._capacity for index in self._past_indices]
observ = self._buffer[indices]
if self._flatten:
observ = np.reshape(observ, (-1,) + observ.shape[2:])
return observ
class FrameDelta(object):
"""Convert the observation to a difference from the previous observation."""
def __init__(self, env):
self._env = env
self._last = None
def __getattr__(self, name):
return getattr(self._env, name)
@property
def observation_space(self):
low = self._env.observation_space.low
high = self._env.observation_space.high
low, high = low - high, high - low
return gym.spaces.Box(low, high)
def step(self, action):
observ, reward, done, info = self._env.step(action)
delta = observ - self._last
self._last = observ
return delta, reward, done, info
def reset(self):
observ = self._env.reset()
self._last = observ
return observ
class RangeNormalize(object):
"""Normalize the specialized observation and action ranges to [-1, 1]."""
def __init__(self, env, observ=None, action=None):
self._env = env
self._should_normalize_observ = (
observ is not False and self._is_finite(self._env.observation_space))
if observ is True and not self._should_normalize_observ:
raise ValueError('Cannot normalize infinite observation range.')
if observ is None and not self._should_normalize_observ:
tf.logging.info('Not normalizing infinite observation range.')
self._should_normalize_action = (
action is not False and self._is_finite(self._env.action_space))
if action is True and not self._should_normalize_action:
raise ValueError('Cannot normalize infinite action range.')
if action is None and not self._should_normalize_action:
tf.logging.info('Not normalizing infinite action range.')
def __getattr__(self, name):
return getattr(self._env, name)
@property
def observation_space(self):
space = self._env.observation_space
if not self._should_normalize_observ:
return space
return gym.spaces.Box(-np.ones(space.shape), np.ones(space.shape))
@property
def action_space(self):
space = self._env.action_space
if not self._should_normalize_action:
return space
return gym.spaces.Box(-np.ones(space.shape), np.ones(space.shape))
def step(self, action):
if self._should_normalize_action:
action = self._denormalize_action(action)
observ, reward, done, info = self._env.step(action)
if self._should_normalize_observ:
observ = self._normalize_observ(observ)
return observ, reward, done, info
def reset(self):
observ = self._env.reset()
if self._should_normalize_observ:
observ = self._normalize_observ(observ)
return observ
def _denormalize_action(self, action):
min_ = self._env.action_space.low
max_ = self._env.action_space.high
action = (action + 1) / 2 * (max_ - min_) + min_
return action
def _normalize_observ(self, observ):
min_ = self._env.observation_space.low
max_ = self._env.observation_space.high
observ = 2 * (observ - min_) / (max_ - min_) - 1
return observ
def _is_finite(self, space):
return np.isfinite(space.low).all() and np.isfinite(space.high).all()
class ClipAction(object):
"""Clip out of range actions to the action space of the environment."""
def __init__(self, env):
self._env = env
def __getattr__(self, name):
return getattr(self._env, name)
@property
def action_space(self):
shape = self._env.action_space.shape
return gym.spaces.Box(-np.inf * np.ones(shape), np.inf * np.ones(shape))
def step(self, action):
action_space = self._env.action_space
action = np.clip(action, action_space.low, action_space.high)
return self._env.step(action)
class LimitDuration(object):
"""End episodes after specified number of steps."""
def __init__(self, env, duration):
self._env = env
self._duration = duration
self._step = None
def __getattr__(self, name):
return getattr(self._env, name)
def step(self, action):
if self._step is None:
raise RuntimeError('Must reset environment.')
observ, reward, done, info = self._env.step(action)
self._step += 1
if self._step >= self._duration:
done = True
self._step = None
return observ, reward, done, info
def reset(self):
self._step = 0
return self._env.reset()
class ExternalProcess(object):
"""Step environment in a separate process for lock free paralellism."""
# Message types for communication via the pipe.
_ACCESS = 1
_CALL = 2
_RESULT = 3
_EXCEPTION = 4
_CLOSE = 5
def __init__(self, constructor):
"""Step environment in a separate process for lock free paralellism.
The environment will be created in the external process by calling the
specified callable. This can be an environment class, or a function
creating the environment and potentially wrapping it. The returned
environment should not access global variables.
Args:
constructor: Callable that creates and returns an OpenAI gym environment.
Attributes:
observation_space: The cached observation space of the environment.
action_space: The cached action space of the environment.
"""
self._conn, conn = multiprocessing.Pipe()
self._process = multiprocessing.Process(
target=self._worker, args=(constructor, conn))
atexit.register(self.close)
self._process.start()
self._observ_space = None
self._action_space = None
@property
def observation_space(self):
if not self._observ_space:
self._observ_space = self.__getattr__('observation_space')
return self._observ_space
@property
def action_space(self):
if not self._action_space:
self._action_space = self.__getattr__('action_space')
return self._action_space
def __getattr__(self, name):
"""Request an attribute from the environment.
Note that this involves communication with the external process, so it can
be slow.
Args:
name: Attribute to access.
Returns:
Value of the attribute.
"""
self._conn.send((self._ACCESS, name))
return self._receive()
def call(self, name, *args, **kwargs):
"""Asynchronously call a method of the external environment.
Args:
name: Name of the method to call.
*args: Positional arguments to forward to the method.
**kwargs: Keyword arguments to forward to the method.
Returns:
Promise object that blocks and provides the return value when called.
"""
payload = name, args, kwargs
self._conn.send((self._CALL, payload))
return self._receive
def close(self):
"""Send a close message to the external process and join it."""
try:
self._conn.send((self._CLOSE, None))
self._conn.close()
except IOError:
# The connection was already closed.
pass
self._process.join()
def step(self, action, blocking=True):
"""Step the environment.
Args:
action: The action to apply to the environment.
blocking: Whether to wait for the result.
Returns:
Transition tuple when blocking, otherwise callable that returns the
transition tuple.
"""
promise = self.call('step', action)
if blocking:
return promise()
else:
return promise
def reset(self, blocking=True):
"""Reset the environment.
Args:
blocking: Whether to wait for the result.
Returns:
New observation when blocking, otherwise callable that returns the new
observation.
"""
promise = self.call('reset')
if blocking:
return promise()
else:
return promise
def _receive(self):
"""Wait for a message from the worker process and return its payload.
Raises:
Exception: An exception was raised inside the worker process.
KeyError: The reveived message is of an unknown type.
Returns:
Payload object of the message.
"""
message, payload = self._conn.recv()
# Re-raise exceptions in the main process.
if message == self._EXCEPTION:
stacktrace = payload
raise Exception(stacktrace)
if message == self._RESULT:
return payload
raise KeyError('Received message of unexpected type {}'.format(message))
def _worker(self, constructor, conn):
"""The process waits for actions and sends back environment results.
Args:
constructor: Constructor for the OpenAI Gym environment.
conn: Connection for communication to the main process.
"""
try:
env = constructor()
while True:
try:
# Only block for short times to have keyboard exceptions be raised.
if not conn.poll(0.1):
continue
message, payload = conn.recv()
except (EOFError, KeyboardInterrupt):
break
if message == self._ACCESS:
name = payload
result = getattr(env, name)
conn.send((self._RESULT, result))
continue
if message == self._CALL:
name, args, kwargs = payload
result = getattr(env, name)(*args, **kwargs)
conn.send((self._RESULT, result))
continue
if message == self._CLOSE:
assert payload is None
break
raise KeyError('Received message of unknown type {}'.format(message))
except Exception: # pylint: disable=broad-except
stacktrace = ''.join(traceback.format_exception(*sys.exc_info()))
tf.logging.error('Error in environment process: {}'.format(stacktrace))
conn.send((self._EXCEPTION, stacktrace))
conn.close()
class ConvertTo32Bit(object):
"""Convert data types of an OpenAI Gym environment to 32 bit."""
def __init__(self, env):
"""Convert data types of an OpenAI Gym environment to 32 bit.
Args:
env: OpenAI Gym environment.
"""
self._env = env
def __getattr__(self, name):
"""Forward unimplemented attributes to the original environment.
Args:
name: Attribute that was accessed.
Returns:
Value behind the attribute name in the wrapped environment.
"""
return getattr(self._env, name)
def step(self, action):
"""Forward action to the wrapped environment.
Args:
action: Action to apply to the environment.
Raises:
ValueError: Invalid action.
Returns:
Converted observation, converted reward, done flag, and info object.
"""
observ, reward, done, info = self._env.step(action)
observ = self._convert_observ(observ)
reward = self._convert_reward(reward)
return observ, reward, done, info
def reset(self):
"""Reset the environment and convert the resulting observation.
Returns:
Converted observation.
"""
observ = self._env.reset()
observ = self._convert_observ(observ)
return observ
def _convert_observ(self, observ):
"""Convert the observation to 32 bits.
Args:
observ: Numpy observation.
Raises:
ValueError: Observation contains infinite values.
Returns:
Numpy observation with 32-bit data type.
"""
if not np.isfinite(observ).all():
raise ValueError('Infinite observation encountered.')
if observ.dtype == np.float64:
return observ.astype(np.float32)
if observ.dtype == np.int64:
return observ.astype(np.int32)
return observ
def _convert_reward(self, reward):
"""Convert the reward to 32 bits.
Args:
reward: Numpy reward.
Raises:
ValueError: Rewards contain infinite values.
Returns:
Numpy reward with 32-bit data type.
"""
if not np.isfinite(reward).all():
raise ValueError('Infinite reward encountered.')
return np.array(reward, dtype=np.float32)

10
examples/pybullet/gym/pybullet_envs/agents/train_ppo.py
View File

@ -24,15 +24,14 @@ from __future__ import division
from __future__ import print_function
import datetime
import functools
import os
import gym
import tensorflow as tf
from agents import tools
from . import tools
from . import configs
from agents.scripts import utility
from . import utility
def _create_environment(config):
@ -73,7 +72,7 @@ def _define_loop(graph, logdir, train_steps, eval_steps):
graph.force_reset)
loop.add_phase(
'train', graph.done, graph.score, graph.summary, train_steps,
report_every=None,
report_every=train_steps,
log_every=train_steps // 2,
checkpoint_every=None,
feed={graph.is_training: True})
@ -100,9 +99,6 @@ def train(config, env_processes):
Evaluation scores.
"""
tf.reset_default_graph()
with config.unlocked:
config.policy_optimizer = getattr(tf.train, config.policy_optimizer)
config.value_optimizer = getattr(tf.train, config.value_optimizer)
if config.update_every % config.num_agents:
tf.logging.warn('Number of agents should divide episodes per update.')
with tf.device('/cpu:0'):

190
examples/pybullet/gym/pybullet_envs/agents/utility.py Normal file
View File

@ -0,0 +1,190 @@
# Copyright 2017 The TensorFlow Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utilities for using reinforcement learning algorithms."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import os
import re
import ruamel.yaml as yaml
import tensorflow as tf
from . import tools
def define_simulation_graph(batch_env, algo_cls, config):
"""Define the algortihm and environment interaction.
Args:
batch_env: In-graph environments object.
algo_cls: Constructor of a batch algorithm.
config: Configuration object for the algorithm.
Returns:
Object providing graph elements via attributes.
"""
# pylint: disable=unused-variable
step = tf.Variable(0, False, dtype=tf.int32, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
should_log = tf.placeholder(tf.bool, name='should_log')
do_report = tf.placeholder(tf.bool, name='do_report')
force_reset = tf.placeholder(tf.bool, name='force_reset')
algo = algo_cls(batch_env, step, is_training, should_log, config)
done, score, summary = tools.simulate(
batch_env, algo, should_log, force_reset)
message = 'Graph contains {} trainable variables.'
tf.logging.info(message.format(tools.count_weights()))
# pylint: enable=unused-variable
return tools.AttrDict(locals())
def define_batch_env(constructor, num_agents, env_processes):
"""Create environments and apply all desired wrappers.
Args:
constructor: Constructor of an OpenAI gym environment.
num_agents: Number of environments to combine in the batch.
env_processes: Whether to step environment in external processes.
Returns:
In-graph environments object.
"""
with tf.variable_scope('environments'):
if env_processes:
envs = [
tools.wrappers.ExternalProcess(constructor)
for _ in range(num_agents)]
else:
envs = [constructor() for _ in range(num_agents)]
batch_env = tools.BatchEnv(envs, blocking=not env_processes)
batch_env = tools.InGraphBatchEnv(batch_env)
return batch_env
def define_saver(exclude=None):
"""Create a saver for the variables we want to checkpoint.
Args:
exclude: List of regexes to match variable names to exclude.
Returns:
Saver object.
"""
variables = []
exclude = exclude or []
exclude = [re.compile(regex) for regex in exclude]
for variable in tf.global_variables():
if any(regex.match(variable.name) for regex in exclude):
continue
variables.append(variable)
saver = tf.train.Saver(variables, keep_checkpoint_every_n_hours=5)
return saver
def initialize_variables(sess, saver, logdir, checkpoint=None, resume=None):
"""Initialize or restore variables from a checkpoint if available.
Args:
sess: Session to initialize variables in.
saver: Saver to restore variables.
logdir: Directory to search for checkpoints.
checkpoint: Specify what checkpoint name to use; defaults to most recent.
resume: Whether to expect recovering a checkpoint or starting a new run.
Raises:
ValueError: If resume expected but no log directory specified.
RuntimeError: If no resume expected but a checkpoint was found.
"""
sess.run(tf.group(
tf.local_variables_initializer(),
tf.global_variables_initializer()))
if resume and not (logdir or checkpoint):
raise ValueError('Need to specify logdir to resume a checkpoint.')
if logdir:
state = tf.train.get_checkpoint_state(logdir)
if checkpoint:
checkpoint = os.path.join(logdir, checkpoint)
if not checkpoint and state and state.model_checkpoint_path:
checkpoint = state.model_checkpoint_path
if checkpoint and resume is False:
message = 'Found unexpected checkpoint when starting a new run.'
raise RuntimeError(message)
if checkpoint:
saver.restore(sess, checkpoint)
def save_config(config, logdir=None):
"""Save a new configuration by name.
If a logging directory is specified, is will be created and the configuration
will be stored there. Otherwise, a log message will be printed.
Args:
config: Configuration object.
logdir: Location for writing summaries and checkpoints if specified.
Returns:
Configuration object.
"""
if logdir:
with config.unlocked:
config.logdir = logdir
message = 'Start a new run and write summaries and checkpoints to {}.'
tf.logging.info(message.format(config.logdir))
tf.gfile.MakeDirs(config.logdir)
config_path = os.path.join(config.logdir, 'config.yaml')
with tf.gfile.FastGFile(config_path, 'w') as file_:
yaml.dump(config, file_, default_flow_style=False)
else:
message = (
'Start a new run without storing summaries and checkpoints since no '
'logging directory was specified.')
tf.logging.info(message)
return config
def load_config(logdir):
"""Load a configuration from the log directory.
Args:
logdir: The logging directory containing the configuration file.
Raises:
IOError: The logging directory does not contain a configuration file.
Returns:
Configuration object.
"""
config_path = logdir and os.path.join(logdir, 'config.yaml')
if not config_path or not tf.gfile.Exists(config_path):
message = (
'Cannot resume an existing run since the logging directory does not '
'contain a configuration file.')
raise IOError(message)
with tf.gfile.FastGFile(config_path, 'r') as file_:
config = yaml.load(file_, Loader=yaml.Loader)
message = 'Resume run and write summaries and checkpoints to {}.'
tf.logging.info(message.format(config.logdir))
return config
def set_up_logging():
"""Configure the TensorFlow logger."""
tf.logging.set_verbosity(tf.logging.INFO)
logging.getLogger('tensorflow').propagate = False

8
examples/pybullet/gym/pybullet_envs/agents/visualize_ppo.py
View File

@ -24,14 +24,13 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import os
import gym
import tensorflow as tf
from agents import tools
from agents.scripts import utility
from . import tools
from . import utility
def _create_environment(config, outdir):
@ -97,9 +96,6 @@ def visualize(
env_processes: Whether to step environments in separate processes.
"""
config = utility.load_config(logdir)
with config.unlocked:
config.policy_optimizer = getattr(tf.train, config.policy_optimizer)
config.value_optimizer = getattr(tf.train, config.value_optimizer)
with tf.device('/cpu:0'):
batch_env = utility.define_batch_env(
lambda: _create_environment(config, outdir),

2
setup.py
View File

@ -441,7 +441,7 @@ print("-----")
setup(
name = 'pybullet',
version='1.6.5',
version='1.6.6',
description='Official Python Interface for the Bullet Physics SDK specialized for Robotics Simulation and Reinforcement Learning',
long_description='pybullet is an easy to use Python module for physics simulation, robotics and deep reinforcement learning based on the Bullet Physics SDK. With pybullet you can load articulated bodies from URDF, SDF and other file formats. pybullet provides forward dynamics simulation, inverse dynamics computation, forward and inverse kinematics and collision detection and ray intersection queries. Aside from physics simulation, pybullet supports to rendering, with a CPU renderer and OpenGL visualization and support for virtual reality headsets.',
url='https://github.com/bulletphysics/bullet3',

70
src/LinearMath/btQuickprof.cpp
View File

@ -680,47 +680,51 @@ void CProfileManager::dumpAll()
CProfileManager::Release_Iterator(profileIterator);
}
// clang-format off
#if defined(_WIN32) && (defined(__MINGW32__) || defined(__MINGW64__))
#define BT_HAVE_TLS 1
#elif __APPLE__ && !TARGET_OS_IPHONE
// TODO: Modern versions of iOS support TLS now with updated version checking.
#define BT_HAVE_TLS 1
#elif __linux__
#define BT_HAVE_TLS 1
#endif
// __thread is broken on Andorid clang until r12b. See
// https://github.com/android-ndk/ndk/issues/8
#if defined(__ANDROID__) && defined(__clang__)
#if __has_include(<android/ndk-version.h>)
#include <android/ndk-version.h>
#endif // __has_include(<android/ndk-version.h>)
#if defined(__NDK_MAJOR__) && \
((__NDK_MAJOR__ < 12) || ((__NDK_MAJOR__ == 12) && (__NDK_MINOR__ < 1)))
#undef BT_HAVE_TLS
#endif
#endif // defined(__ANDROID__) && defined(__clang__)
// clang-format on
unsigned int btQuickprofGetCurrentThreadIndex2() {
const unsigned int kNullIndex = ~0U;
unsigned int btQuickprofGetCurrentThreadIndex2()
{
#if BT_THREADSAFE
return btGetCurrentThreadIndex();
#else // #if BT_THREADSAFE
const unsigned int kNullIndex = ~0U;
#ifdef _WIN32
#if defined(__MINGW32__) || defined(__MINGW64__)
static __thread unsigned int sThreadIndex = kNullIndex;
#else
__declspec( thread ) static unsigned int sThreadIndex = kNullIndex;
#endif
return btGetCurrentThreadIndex();
#else
#ifdef __APPLE__
#if TARGET_OS_IPHONE
unsigned int sThreadIndex = 0;
return -1;
#else
static __thread unsigned int sThreadIndex = kNullIndex;
#endif
#else//__APPLE__
#if __linux__
static __thread unsigned int sThreadIndex = kNullIndex;
#if defined(BT_HAVE_TLS)
static __thread unsigned int sThreadIndex = kNullIndex;
#elif defined(_WIN32)
__declspec(thread) static unsigned int sThreadIndex = kNullIndex;
#else
unsigned int sThreadIndex = 0;
return -1;
unsigned int sThreadIndex = 0;
return -1;
#endif
#endif//__APPLE__
#endif
static int gThreadCounter=0;
if ( sThreadIndex == kNullIndex )
{
sThreadIndex = gThreadCounter++;
}
return sThreadIndex;
#endif // #else // #if BT_THREADSAFE
static int gThreadCounter = 0;
if (sThreadIndex == kNullIndex) {
sThreadIndex = gThreadCounter++;
}
return sThreadIndex;
#endif //BT_THREADSAFE
}
void btEnterProfileZoneDefault(const char* name)