Update description of MAPO.

README.md

@@ -16,7 +16,7 @@ The framework can be used to learn semantic parsing and program synthesis from w
 
 ## Memory Augmented Policy Optimization (MAPO)
 
-We use <a href="https://arxiv.org/abs/1807.02322">Memory Augmented Policy Optimization (MAPO)</a> to train NSM. It is a new policy optimization method that incorporates a memory buffer of promising trajectories to reduce the variance of policy gradient estimates for deterministic environments with discrete actions, for example, structured prediction and combinatorial optimization. 
+We use <a href="https://arxiv.org/abs/1807.02322">Memory Augmented Policy Optimization (MAPO)</a> to train NSM. It is a new policy optimization method that uses a memory buffer of promising trajectories to accelerate and stabilize policy gradient training. It is well suited for deterministic environments with discrete actions, for example, structured prediction and combinatorial optimization. 
 
 <div align="middle"><img src="https://github.com/crazydonkey200/neural-symbolic-machines/blob/master/images/mapo.png" width="80%"></div>