Merge remote-tracking branch 'bp/master'

examples/pybullet/gym/agents/simpleAgent.py

@@ -16,7 +16,7 @@ class SimpleAgent():
       session,
       ckpt_path,
       actor_layer_size,
-      observation_dim=31
+      observation_dim=28
   ):
     self._ckpt_path = ckpt_path
     self._session = session

examples/pybullet/gym/agents/simpleAgentWithSonnet.py

@@ -18,7 +18,7 @@ class SimpleAgent():
       session,
       ckpt_path,
       actor_layer_size,
-      observation_size=(31,),
+      observation_size=(28,),
       action_size=8,
   ):
     self._ckpt_path = ckpt_path
@@ -30,7 +30,7 @@ class SimpleAgent():
 
   def _build(self):
     self._agent_net = actor_net.ActorNetwork(self._actor_layer_size, self._action_size)
-    self._obs = tf.placeholder(tf.float32, (31,))
+    self._obs = tf.placeholder(tf.float32, self._observation_size)
     with tf.name_scope('Act'):
       batch_obs = snt.nest.pack_iterable_as(self._obs,
                                             snt.nest.map(lambda x: tf.expand_dims(x, 0),

examples/pybullet/gym/data/agent/tf_graph_data/checkpoint

@@ -1,2 +0,0 @@
-model_checkpoint_path: "tf_graph_data_converted.ckpt-0"
-all_model_checkpoint_paths: "tf_graph_data_converted.ckpt-0"

examples/pybullet/gym/envs/bullet/minitaurGymEnv.py

@@ -16,10 +16,14 @@ class MinitaurGymEnv(gym.Env):
   def __init__(self,
                urdfRoot="",
                actionRepeat=1,
+               isEnableSelfCollision=True,
+               motorVelocityLimit=10.0,
                render=False):
     self._timeStep = 0.01
     self._urdfRoot = urdfRoot
     self._actionRepeat = actionRepeat
+    self._motorVelocityLimit = motorVelocityLimit
+    self._isEnableSelfCollision = isEnableSelfCollision
     self._observation = []
     self._envStepCounter = 0
     self._render = render
@@ -44,7 +48,7 @@ class MinitaurGymEnv(gym.Env):
     p.setTimeStep(self._timeStep)
     p.loadURDF("%splane.urdf" % self._urdfRoot)
     p.setGravity(0,0,-10)
-    self._minitaur = minitaur_new.Minitaur(self._urdfRoot)
+    self._minitaur = minitaur_new.Minitaur(urdfRootPath=self._urdfRoot, timeStep=self._timeStep, isEnableSelfCollision=self._isEnableSelfCollision, motorVelocityLimit=self._motorVelocityLimit)
     self._envStepCounter = 0
     self._lastBasePosition = [0, 0, 0]
     for i in range(100):

examples/pybullet/gym/envs/bullet/minitaur_new.py

@@ -4,8 +4,12 @@ import copy
 import math
 
 class Minitaur:
-  def __init__(self, urdfRootPath=''):
+
+  def __init__(self, urdfRootPath='', timeStep=0.01, isEnableSelfCollision=True, motorVelocityLimit=10.0):
     self.urdfRootPath = urdfRootPath
+    self.isEnableSelfCollision = isEnableSelfCollision
+    self.motorVelocityLimit = motorVelocityLimit
+    self.timeStep = timeStep
     self.reset()
 
   def buildJointNameToIdDict(self):
@@ -27,8 +31,10 @@ class Minitaur:
     self.motorIdList.append(self.jointNameToId['motor_back_rightR_joint'])
 
   def reset(self):
-#    self.quadruped = p.loadURDF("%squadruped/minitaur.urdf" % self.urdfRootPath, [0,0,.2], flags=p.URDF_USE_SELF_COLLISION)
-    self.quadruped = p.loadURDF("%squadruped/minitaur.urdf" % self.urdfRootPath, [0,0,.2])
+    if self.isEnableSelfCollision:
+      self.quadruped = p.loadURDF("%s/quadruped/minitaur.urdf" % self.urdfRootPath, [0,0,.2], flags=p.URDF_USE_SELF_COLLISION)
+    else:
+      self.quadruped = p.loadURDF("%s/quadruped/minitaur.urdf" % self.urdfRootPath, [0,0,.2])
     self.kp = 1
     self.kd = 1
     self.maxForce = 3.5
@@ -116,11 +122,15 @@ class Minitaur:
     observation.extend(self.getMotorVelocities().tolist())
     observation.extend(self.getMotorTorques().tolist())
     observation.extend(list(self.getBaseOrientation()))
-    observation.extend(list(self.getBasePosition()))
     return observation
 
 
   def applyAction(self, motorCommands):
+    if self.motorVelocityLimit < np.inf:
+      currentMotorAngle = self.getMotorAngles()
+      motorCommandsMax = currentMotorAngle + self.timeStep * self.motorVelocityLimit
+      motorCommandsMin = currentMotorAngle - self.timeStep * self.motorVelocityLimit
+      motorCommands = np.clip(motorCommands, motorCommandsMin, motorCommandsMax)
     motorCommandsWithDir = np.multiply(motorCommands, self.motorDir)
 #    print('action: {}'.format(motorCommands))
 #    print('motor: {}'.format(motorCommandsWithDir))

examples/pybullet/gym/minitaurGymEnvTest.py

@@ -14,10 +14,9 @@ from envs.bullet.minitaurGymEnv import MinitaurGymEnv
 try:
   import sonnet
   from agents import simpleAgentWithSonnet as agent_lib
-  ckpt_path = 'data/agent/tf_graph_data/tf_graph_data_converted.ckpt-0'
 except ImportError:
   from agents import simpleAgent as agent_lib
-  ckpt_path = 'data/agent/tf_graph_data/tf_graph_data.ckpt'
+
   
 def testSinePolicy():
   """Tests sine policy
@@ -60,10 +59,10 @@ def testDDPGPolicy():
   environment = MinitaurGymEnv(render=True)
   sum_reward = 0
   steps = 1000
-
-  observation_shape = (31,)
+  ckpt_path = 'data/agent/tf_graph_data/tf_graph_data_converted.ckpt-0'
+  observation_shape = (28,)
   action_size = 8
-  actor_layer_size = (100, 181)
+  actor_layer_size = (297, 158)
   n_steps = 0
   tf.reset_default_graph()
   with tf.Session() as session: