Delete run_cartpole.py

policy gradient/run_cartpole.py

@@ -1,59 +0,0 @@
-
-
-import gym
-from RL_brain import PolicyGradient
-import matplotlib.pyplot as plt
-
-DISPLAY_REWARD_THRESHOLD = 400  # renders environment if total episode reward is greater then this threshold
-RENDER = False  # rendering wastes time
-
-env = gym.make('CartPole-v0')
-env.seed(1)     # reproducible, general Policy gradient has high variance
-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=0.99,
-    # output_graph=True,
-)
-
-for i_episode in range(3000):
-
-    observation = env.reset()
-
-    while True:
-        if RENDER: env.render()
-
-        action = RL.choose_action(observation)
-
-        observation_, reward, done, info = env.step(action)
-
-        RL.store_transition(observation, action, reward)
-
-        if done:
-            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()
-
-            if i_episode == 0:
-                plt.plot(vt)    # plot the episode vt
-                plt.xlabel('episode steps')
-                plt.ylabel('normalized state-action value')
-                plt.show()
-            break
-
-        observation = observation_