Create mountaincar.py

policy gradient/mountaincar.py

@@ -0,0 +1,62 @@
+
+
+import gym
+from RL_brain import PolicyGradient
+import matplotlib.pyplot as plt
+
+DISPLAY_REWARD_THRESHOLD = -2000  # renders environment if total episode reward is greater then this threshold
+
+RENDER = False  # rendering wastes time
+
+env = gym.make('MountainCar-v0')
+env.seed(1)     
+env = env.unwrapped
+
+print(env.action_space)
+print(env.observation_space)
+print(env.observation_space.high)
+print(env.observation_space.low)
+
+RL = PolicyGradient(
+    n_actions=env.action_space.n,
+    n_features=env.observation_space.shape[0],
+    learning_rate=0.02,
+    reward_decay=0.995,
+    # output_graph=True,
+)
+
+for i_episode in range(1000):
+
+    observation = env.reset()
+
+    while True:
+        if RENDER: env.render()
+
+        action = RL.choose_action(observation)
+
+        observation_, reward, done, info = env.step(action)     # reward = -1 in all cases
+
+        RL.store_transition(observation, action, reward)
+
+        if done:
+            # calculate running reward
+            ep_rs_sum = sum(RL.ep_rs)
+            if 'running_reward' not in globals():
+                running_reward = ep_rs_sum
+            else:
+                running_reward = running_reward * 0.99 + ep_rs_sum * 0.01
+            if running_reward > DISPLAY_REWARD_THRESHOLD: RENDER = True     # rendering
+
+            print("episode:", i_episode, "  reward:", int(running_reward))
+
+            vt = RL.learn()  # train
+
+            if i_episode == 30:
+                plt.plot(vt)  # plot the episode vt
+                plt.xlabel('episode steps')
+                plt.ylabel('normalized state-action value')
+                plt.show()
+
+            break
+
+        observation = observation_