readme for training atari models

examples/Atari2600/common.py

@@ -57,7 +57,7 @@ def eval_with_funcs(predict_funcs, nr_eval):
                     return
                 self.queue_put_stoppable(self.q, score)
 
-    q = queue.Queue(maxsize=2)
+    q = queue.Queue()
     threads = [Worker(f, q) for f in predict_funcs]
 
     for k in threads:
@@ -68,12 +68,15 @@ def eval_with_funcs(predict_funcs, nr_eval):
         for _ in tqdm(range(nr_eval), **get_tqdm_kwargs()):
             r = q.get()
             stat.feed(r)
-    except:
-        logger.exception("Eval")
-    finally:
         logger.info("Waiting for all the workers to finish the last run...")
         for k in threads: k.stop()
         for k in threads: k.join()
+        while q.qsize():
+            r = q.get()
+            stat.feed(r)
+    except:
+        logger.exception("Eval")
+    finally:
         if stat.count > 0:
             return (stat.average, stat.max)
         return (0, 0)

examples/OpenAIGym/README.md

+# To train an Atari game in gym:
 
-# To run a pretrained Batch-A3C atari model for 100 episodes:
+`./train-atari.py --env Breakout-v0 --gpu 0`
 
-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"`
+# To run a pretrained Atari model for 100 episodes:
+
+1. Download models from [model zoo](https://drive.google.com/open?id=0B9IPQTvr2BBkS0VhX0xmS1c5aFk)
+2. `ENV=NAME_OF_ENV ./run-atari.py --load "$ENV".tfmodel --env "$ENV"`
 
 Models are available for the following gym atari environments (click links for videos):