added a policy trained by DDPG

examples/pybullet/gym/minitaurGymEnvTest.py

@@ -0,0 +1,85 @@
+'''
+A test for minitaurGymEnv
+'''
+
+import gym
+import numpy as np
+import math
+
+import numpy as np
+import tensorflow as tf
+
+from envs.bullet.minitaurGymEnv import MinitaurGymEnv
+from agents import simpleAgent
+
+def testSinePolicy():
+  """Tests sine policy
+    """
+  np.random.seed(47)
+
+  environment = MinitaurGymEnv(render=True)
+  sum_reward = 0
+  steps = 1000
+  amplitude1Bound = 0.5
+  amplitude2Bound = 0.15
+  speed = 40
+
+  for stepCounter in range(steps):
+    t = float(stepCounter) * environment._timeStep
+
+    if (t < 1):
+      amplitude1 = 0
+      amplitude2 = 0
+    else:
+      amplitude1 = amplitude1Bound
+      amplitude2 = amplitude2Bound
+    a1 = math.sin(t*speed)*amplitude1
+    a2 = math.sin(t*speed+3.14)*amplitude1
+    a3 = math.sin(t*speed)*amplitude2
+    a4 = math.sin(t*speed+3.14)*amplitude2
+
+    action = [a1, a2, a2, a1, a3, a4, a4, a3]
+
+    state, reward, done, info = environment.step(action)
+    sum_reward += reward
+    if done:
+      environment.reset()
+      print("sum reward: ", sum_reward)
+
+
+def testDDPGPolicy():
+  """Tests sine policy
+    """
+  environment = MinitaurGymEnv(render=True)
+  sum_reward = 0
+  steps = 1000
+  ckpt_path = 'data/agent/tf_graph_data/tf_graph_data.ckpt'
+  observation_shape = (31,)
+  action_size = 8
+  actor_layer_sizes = (100, 181)
+  n_steps = 0
+  tf.reset_default_graph()
+  with tf.Session() as session:
+    agent = simpleAgent.SimpleAgent(session, ckpt_path,
+                                    actor_layer_sizes,
+                                    observation_size=observation_shape,
+                                    action_size=action_size)
+    state = environment.reset()
+    action = agent(state)
+    for _ in range(steps):
+      n_steps += 1
+      state, reward, done, info = environment.step(action)
+      action = agent(state)
+      sum_reward += reward
+      if done:
+        environment.reset()
+        n_steps += 1
+        print("total reward: ", sum_reward)
+        print("total steps:  ", n_steps)
+        sum_reward = 0
+        n_steps = 0
+        return
+
+
+testDDPGPolicy()
+#testSinePolicy()