misc update

examples/OpenAIGym/README.md

 
-# To run pretrained model:
+# To run pretrained atari model for 100 episodes:
 
 1. install [tensorpack](https://github.com/ppwwyyxx/tensorpack)
 2. Download models from [model zoo](https://drive.google.com/open?id=0B9IPQTvr2BBkS0VhX0xmS1c5aFk)
 3. `ENV=NAME_OF_ENV ./run-atari.py --load "$ENV".tfmodel --env "$ENV"`
 
-Models are available for the following gym environments:
+Models are available for the following gym atari environments:
 
 + [Breakout-v0](https://gym.openai.com/envs/Breakout-v0)
 + [AirRaid-v0](https://gym.openai.com/envs/AirRaid-v0)
++ [Asterix-v0](https://gym.openai.com/envs/Asterix-v0)
++ [Amidar-v0](https://gym.openai.com/envs/Asterix-v0)
++ [Seaquest-v0](https://gym.openai.com/envs/Seaquest-v0)
 
 Note that atari game settings in gym is more difficult than the settings DeepMind papers, therefore the scores are not comparable.

examples/OpenAIGym/run-atari.py

@@ -22,18 +22,14 @@ IMAGE_SHAPE3 = IMAGE_SIZE + (CHANNEL,)
 NUM_ACTIONS = None
 ENV_NAME = None
 
-def get_player(viz=False, train=False, dumpdir=None):
+def get_player(dumpdir=None):
     pl = GymEnv(ENV_NAME, dumpdir=dumpdir)
-    def func(img):
-        return cv2.resize(img, IMAGE_SIZE[::-1])
-    pl = MapPlayerState(pl, func)
+    pl = MapPlayerState(pl, lambda img: cv2.resize(img, IMAGE_SIZE[::-1]))
 
     global NUM_ACTIONS
     NUM_ACTIONS = pl.get_action_space().num_actions()
 
     pl = HistoryFramePlayer(pl, FRAME_HISTORY)
-    if not train:
-        pl = PreventStuckPlayer(pl, 30, 1)
     return pl
 
 class MySimulatorWorker(SimulatorProcess):
@@ -69,10 +65,6 @@ class Model(ModelDesc):
         policy = self._get_NN_prediction(state, is_training)
         self.logits = tf.nn.softmax(policy, name='logits')
 
-    def get_gradient_processor(self):
-        return [MapGradient(lambda grad: tf.clip_by_average_norm(grad, 0.1)),
-                SummaryGradient()]
-
 def play_one_episode(player, func, verbose=False):
     def f(s):
         spc = player.get_action_space()
@@ -109,5 +101,5 @@ if __name__ == '__main__':
             model=Model(),
             session_init=SaverRestore(args.load),
             input_var_names=['state'],
-            output_var_names=['logits:0'])
+            output_var_names=['logits'])
     run_submission(cfg)

tensorpack/callbacks/common.py

@@ -107,9 +107,9 @@ class MinSaver(Callback):
                 "Cannot find a checkpoint state. Do you forget to use ModelSaver?")
         path = chpt.model_checkpoint_path
         newname = os.path.join(logger.LOG_DIR,
-                'max_' if self.reverse else 'min_' + self.monitor_stat)
+                'max-' if self.reverse else 'min-' + self.monitor_stat)
         shutil.copy(path, newname)
-        logger.info("Model with {} {} saved.".format(
+        logger.info("Model with {} '{}' saved.".format(
             'maximum' if self.reverse else 'minimum', self.monitor_stat))
 
 class MaxSaver(MinSaver):