small fix

examples/OpenAIGym/README.md

 
-# Steps to reproduce:
+# To run pretrained model:
 
 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:
+   -
+   -+ [Breakout-v0](https://gym.openai.com/envs/Breakout-v0)
+	 -->
+
+Note that atari game settings in gym is very different from DeepMind papers, therefore the scores are not comparable.

examples/OpenAIGym/run-atari.py

@@ -34,7 +34,6 @@ def get_player(viz=False, train=False, dumpdir=None):
     pl = HistoryFramePlayer(pl, FRAME_HISTORY)
     if not train:
         pl = PreventStuckPlayer(pl, 30, 1)
-    pl = LimitLengthPlayer(pl, 40000)
     return pl
 
 class MySimulatorWorker(SimulatorProcess):

scripts/dump-model-params.py

@@ -33,7 +33,7 @@ with tf.Graph().as_default() as G:
         init = sessinit.ParamRestore(np.load(args.model).item())
     else:
         init = sessinit.SaverRestore(args.model)
-    sess = tf.Session()
+    sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
     init.init(sess)
 
     # dump ...

tensorpack/RL/envbase.py

@@ -88,6 +88,12 @@ class DiscreteActionSpace(ActionSpace):
     def num_actions(self):
         return self.num
 
+    def __repr__(self):
+        return "DiscreteActionSpace({})".format(self.num)
+
+    def __str__(self):
+        return "DiscreteActionSpace({})".format(self.num)
+
 class NaiveRLEnvironment(RLEnvironment):
     """ for testing only"""
     def __init__(self):