Update to follow the new observation format

(follow the vision input of OpenAI ATARI environment)

sample_agent.py

@@ -5,28 +5,29 @@ class Agent(object):
     def __init__(self, dim_action):
         self.dim_action = dim_action
 
-    def act(self, ob, reward, done, vision):
+    def act(self, ob, reward, done, vision_on):
         #print("ACT!")
 
         # Get an Observation from the environment.
         # Each observation vectors are numpy array.
         # focus, opponents, track sensors are scaled into [0, 1]. When the agent
         # is out of the road, sensor variables return -1/200.
         # rpm, wheelSpinVel are raw values and then needed to be preprocessed.
-        # vision is given as a tensor with size of (3, 64, 64) <-- rgb
+        # vision is given as a tensor with size of (64*64, 3) = (4096, 3) <-- rgb
         # and values are in [0, 255]
-        if vision is False:
+        if vision_on is False:
             focus, speedX, speedY, speedZ, opponents, rpm, track, wheelSpinVel = ob
         else:
             focus, speedX, speedY, speedZ, opponents, rpm, track, wheelSpinVel, vision = ob
 
             """ The code below is for checking the vision input. This is very heavy for real-time Control
                 So you may need to remove.
             """
+            print(vision.shape)
             """
             img = np.ndarray((64,64,3))
             for i in range(3):
-                img[:, :, i] = 255 - vision[i]
+                img[:, :, i] = 255 - vision[:, i].reshape((64, 64))
 
             plt.imshow(img, origin='lower')
             plt.draw()