initial commit

4_policy_gradient.py

@@ -105,45 +105,44 @@ def learn(x, y, adv):
 
     return loss.data[0]
 
-
 for e in range(10000):
     complete = run_episode(model, e, ENV)
 
     if complete:
         print('complete...!')
         break
 
-import matplotlib.pyplot as plt
-from moviepy.editor import ImageSequenceClip
-
-def botPlay(env):
-    state = env.reset()
-    steps = 0
-    frames = []
-    while True:
-        frame = env.render(mode='rgb_array')
-        frames.append(frame)
-        action = torch.max(model(Variable(FloatTensor([state]))), 1)[1].data[0]
-        next_state, reward, done, _ = env.step(action)
-
-        state = next_state
-        steps += 1
-
-        if done or steps >= 1000:
-            break
-
-    clip = ImageSequenceClip(frames, fps=20)
-    clip.write_gif('4_policy_gradient_play.gif', fps=20)
-
-def plot_durations(d):
-    plt.figure(2)
-    plt.clf()
-    plt.title('Training...')
-    plt.xlabel('Episode')
-    plt.ylabel('Duration')
-    plt.plot(d)
-
-    plt.savefig('4_policy_gradient_score.png')
-
-botPlay(ENV)
-plot_durations(HISTORY)
+# import matplotlib.pyplot as plt
+# from moviepy.editor import ImageSequenceClip
+#
+# def botPlay(env):
+#     state = env.reset()
+#     steps = 0
+#     frames = []
+#     while True:
+#         frame = env.render(mode='rgb_array')
+#         frames.append(frame)
+#         action = torch.max(model(Variable(FloatTensor([state]))), 1)[1].data[0]
+#         next_state, reward, done, _ = env.step(action)
+#
+#         state = next_state
+#         steps += 1
+#
+#         if done or steps >= 1000:
+#             break
+#
+#     clip = ImageSequenceClip(frames, fps=20)
+#     clip.write_gif('4_policy_gradient_play.gif', fps=20)
+#
+# def plot_durations(d):
+#     plt.figure(2)
+#     plt.clf()
+#     plt.title('Training...')
+#     plt.xlabel('Episode')
+#     plt.ylabel('Duration')
+#     plt.plot(d)
+#
+#     plt.savefig('4_policy_gradient_score.png')
+#
+# botPlay(ENV)
+# plot_durations(HISTORY)