use uint8 image in ALE

examples/Atari2600/DQN.py

@@ -40,6 +40,8 @@ EXPLORATION_EPOCH_ANNEAL = 0.01
 END_EXPLORATION = 0.1
 
 MEMORY_SIZE = 1e6
+# NOTE: will consume at least 1e6 * 84 * 84 * 4 bytes = 26G memory.
+# Suggest using tcmalloc to manage memory space better.
 INIT_MEMORY_SIZE = 5e4
 STEP_PER_EPOCH = 10000
 EVAL_EPISODE = 50

examples/Atari2600/atari.py

@@ -54,7 +54,7 @@ class AtariPlayer(RLEnvironment):
             ALEInterface.setLoggerMode(ALEInterface.Logger.Warning)
         except AttributeError:
             if execute_only_once():
-                logger.warn("https://github.com/mgbellemare/Arcade-Learning-Environment/pull/171 is not merged!")
+                logger.warn("You're not using latest ALE")
 
         # avoid simulator bugs: https://github.com/mgbellemare/Arcade-Learning-Environment/issues/86
         with _ALE_LOCK:
@@ -104,14 +104,13 @@ class AtariPlayer(RLEnvironment):
 
     def current_state(self):
         """
-        :returns: a gray-scale (h, w, 1) float32 image
+        :returns: a gray-scale (h, w, 1) uint8 image
         """
         ret = self._grab_raw_image()
         # max-pooled over the last screen
         ret = np.maximum(ret, self.last_raw_screen)
         if self.viz:
             if isinstance(self.viz, float):
-                #m = cv2.resize(ret, (1920,1200))
                 cv2.imshow(self.windowname, ret)
                 time.sleep(self.viz)
         ret = ret[self.height_range[0]:self.height_range[1],:].astype('float32')
@@ -119,7 +118,7 @@ class AtariPlayer(RLEnvironment):
         ret = cv2.cvtColor(ret, cv2.COLOR_RGB2GRAY)
         ret = cv2.resize(ret, self.image_shape)
         ret = np.expand_dims(ret, axis=2)
-        return ret
+        return ret.astype('uint8')  # to save some memory
 
     def get_action_space(self):
         return DiscreteActionSpace(len(self.actions))

tensorpack/RL/expreplay.py

@@ -78,14 +78,16 @@ class ExpReplay(DataFlow, Callback):
 
         with tqdm(total=self.init_memory_size) as pbar:
             while len(self.mem) < self.init_memory_size:
-                #from copy import deepcopy  # quickly fill the memory for debug
-                #self.mem.append(deepcopy(self.mem[0]))
                 self._populate_exp()
                 pbar.update()
         self._init_memory_flag.set()
 
     def _populate_exp(self):
         """ populate a transition by epsilon-greedy"""
+        #if len(self.mem):
+            #from copy import deepcopy  # quickly fill the memory for debug
+            #self.mem.append(deepcopy(self.mem[0]))
+            #return
         old_s = self.player.current_state()
         if self.rng.rand() <= self.exploration:
             act = self.rng.choice(range(self.num_actions))