Initial commit

Divers/renforcement2/CartPole_predict.py

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+import gym
+import numpy as np
+import CartPole_common
+
+env=gym.make("CartPole-v0")
+env._max_episode_steps=5000
+
+q_table=np.load("CartPole_qtable.npy")
+
+for epoch in range(1000):
+    state = env.reset()
+    score = 0
+    while True:
+        env.render()
+        discrete_state=CartPole_common.discretise(state)
+        action=np.argmax(q_table[discrete_state])
+        #if not np.random.randint(5):
+        #    action=np.random.randint(2)
+        state, reward, done, info=env.step(action)
+        score+=reward
+        if done:
+            print('Essai {:05d} Score: {:04d}'.format(epoch, int(score)))
+            break
+
+env.close()