update readme

examples/Atari2600/README.md

@@ -16,9 +16,11 @@ Claimed performance in the paper can be reproduced, on several games I've tested
 ![DQN](curve-breakout.png)
 
 DQN typically took 2 days of training to reach a score of 400 on breakout game.
-My Batch-A3C implementation only took <2 hours (one for training and one for simulation).
+My Batch-A3C implementation only took <2 hours.
 Both were trained on one GPU with an extra GPU for simulation.
-This is probably the fastest RL trainer you'd find.
+<!--
+   -This is probably the fastest RL trainer you'd find.
+	 -->
 
 The x-axis is the number of iterations, not wall time.
 Iteration speed on Tesla M40 is about 9.7it/s for B-A3C.

scripts/plot-point.py

@@ -2,7 +2,7 @@
 # -*- coding: utf-8 -*-
 
 """
-A general curve plotter used to create curves like:
+A general curve plotter to create curves such as:
 https://github.com/ppwwyyxx/tensorpack/tree/master/examples/ResNet
 
 A simplest example:

tensorpack/RL/simulator.py

@@ -27,7 +27,9 @@ except ImportError:
 
 
 class SimulatorProcess(multiprocessing.Process):
-    """ A process that simulates a player """
+    """
+    A process that simulates a player and communicates to master to get the next action
+    """
     __metaclass__ = ABCMeta
 
     def __init__(self, idx, pipe_c2s, pipe_s2c):