Improving Policy Gradient by Exploring Under-appreciated Rewards

Abstract

This paper presents a novel form of policy gradient for model-free reinforcement learning (RL) with improved exploration properties. Current policy-based methods use entropy regularization to encourage undirected exploration of the reward landscape, which is ineffective in high dimensional spaces with sparse rewards. We propose a more directed exploration strategy that promotes exploration of under-appreciated reward regions. An action sequence is considered under-appreciated if its log-probability under the current policy under-estimates its resulting reward. The proposed exploration strategy is easy to implement, requiring only small modifications to the standard REINFORCE algorithm. We evaluate the approach on a set of algorithmic tasks that have long challenged RL methods. We find that our approach reduces hyper-parameter sensitivity and demonstrates significant improvements over baseline methods. Notably, the approach is able to solve a benchmark multi-digit addition task. To our knowledge, this is the first time that a pure RL method has solved addition using only reward feedback.