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https://github.com/bulletphysics/bullet3
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851ca5bfb3
Use python -m pybullet_envs.examples.testEnv --env AntBulletEnv-v0 --render=1 --steps 1000 --resetbenchmark=1 Added environments: HumanoidFlagrunBulletEnv-v0, HumanoidFlagrunHarderBulletEnv-v0, StrikerBulletEnv-v0, ThrowerBulletEnv-v0, PusherBulletEnv-v0, ReacherBulletEnv-v0, CartPoleBulletEnv-v0 and register them to OpenAI Gym. Allow numpy/humanoid_running.py to use abtch or non-batch update (setJointMotorControl2/setJointMotorControlArray)
112 lines
4.2 KiB
Python
112 lines
4.2 KiB
Python
#!/usr/bin/env python
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import argparse # parse input arguments
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import numpy as np # arithmetic library
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import time
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import gym
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from agents import agent_register
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import pybullet as p
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import kerasrl_utils
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np.set_printoptions(precision=3, suppress=True, linewidth=10000)
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def add_opts(parser):
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parser.add_argument('--agent', type=str, default="KerasDQNAgent", help="Agent to be trained with.")
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parser.add_argument('--env', type=str, default="2DDetachedCartPolev0Env", help="Environment to be trained in.")
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parser.add_argument('--use-latest', action='store_true', help="Should the trainer retrain/show with the most recent save?")
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parser.add_argument('--train-for', type=int, default=100, help="The number of epochs to train for.")
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parser.add_argument('--test-for', type=int, default=0, help="The number of epoch to test for.")
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parser.add_argument('--load-file', type=str, default=None, help="The weight file to load for training.")
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parser.add_argument('--save-file', type=str, default=None, help="The weight file to save after training.")
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class Trainer:
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'''
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The trainer class helps to easily set up a gym training session using an agent(representing the learning algorithm and the gym (being the environment)
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'''
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# TODO: Make training fail-safe by catching "not connected to server" and save the current state to disk (see primitive examples, they can do it)
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def __init__(self):
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# initialize random seed
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np.random.seed(int(time.time()))
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cid = p.connect(p.SHARED_MEMORY) # only show graphics if the browser is already running....
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self.visualize = (cid >= 0)
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if cid < 0:
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cid = p.connect(p.DIRECT) # If no shared memory browser is active, we switch to headless mode (DIRECT is much faster)
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def setup_exercise(self, opts):
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# setup agent
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agent = agent_register.make(opts.agent, opts=opts)
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# setup environment
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env = gym.make(opts.env)
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if self.visualize:
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mode = "human"
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else:
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mode = "none"
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env.render(mode=mode)
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# configurations
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env.seed(int(time.time()))
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#env.configureActions(agent.metadata['discrete_actions']) # configure environment to accepts discrete actions
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if agent.metadata['discrete_actions']:
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agent.configure(env.observation_space.shape, env.action_space.n) # configure agent to use the environment properties
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else:
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agent.configure(env.observation_space.shape, env.action_space.shape[0]) # configure agent to use the environment properties
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if opts.use_latest:
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properties = kerasrl_utils.get_latest_save("checkpoints/", opts.agent, opts.env, 0)
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if properties == []:
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print("No previous weight saves found for %s-%s" % (opts.agent, opts.env))
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else:
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opts.load_file = "checkpoints/%s-%s-%s.h5" % (properties[0], properties[1], properties[2])
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print("Continue from [%s] " % opts.load_file)
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if opts.load_file is not None:
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print("loading weights from [%s]" % opts.load_file)
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agent.load_weights(opts.load_file)
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# Okay, now it's time to learn something! We visualize the training here for show, but this
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# slows down training quite a lot. You can always safely abort the training prematurely using
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# Ctrl + C.
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agent.train(env, nb_steps=opts.train_for, visualize=self.visualize, verbosity=1)
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# After training is done, we save the final weights.
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if opts.save_file is not None:
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print("saving weights to [%s]" % opts.save_file)
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agent.save_weights(opts.save_file, overwrite=True)
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# Finally, evaluate our algorithm.
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agent.test(env, nb_episodes=opts.test_for, visualize=self.visualize)
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if __name__ == "__main__":
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"""
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You can also run the trainer as a main class if you want to start your own agent/environment combination. If you know your precise arguments, just run this as your main.
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"""
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trainer = Trainer.Trainer()
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parser = argparse.ArgumentParser()
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# add all parsing options
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Trainer.add_opts(parser)
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opts, unknown = parser.parse_known_args() # parse agent and environment to add their opts
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exec("from agents import %s" % opts.agent) # import agent type
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exec("from envs import %s" % opts.env) # import env type
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exec("%s.add_opts(parser)" % opts.agent)
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exec("%s.add_opts(parser)" % opts.env)
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# parse arguments
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opts, unknown = parser.parse_known_args()
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print("OPTS", opts)
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print("UNKNOWN", unknown)
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trainer.setup_exercise(opts)
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