  Trust Region Policy Optimization (TRPO) is a popular and empirically
successful policy search algorithm in reinforcement learning (RL). It
iteratively solved the surrogate problem which restricts consecutive policies
to be close to each other. TRPO is an on-policy algorithm. On-policy methods
bring many benefits, like the ability to gauge each resulting policy. However,
they typically discard all the knowledge about the policies which existed
before. In this work, we use a replay buffer to borrow from the off-policy
learning setting to TRPO. Entropy regularization is usually used to improve
policy optimization in reinforcement learning. It is thought to aid exploration
and generalization by encouraging more random policy choices. We add an Entropy
regularization term to advantage over {\pi}, accumulated over time steps, in
TRPO. We call this update EnTRPO. Our experiments demonstrate EnTRPO achieves
better performance for controlling a Cart-Pole system compared with the
original TRPO

Machine Learning