Value Enhancement of Reinforcement Learning via Efficient and Robust Trust Region Optimization

Understand

Reinforcement learning (RL) is a powerful machine learning technique that enables an intelligent agent to learn an optimal policy that maximizes the cumulative rewards in sequential decision making.

• Most of methods in the existing literature are developed in \textit{online} settings where the data are easy to collect or simulate.
• Motivated by high stake domains such as mobile health studies with limited and pre-collected data, in this paper, we study \textit{offline} reinforcement learning methods.
• To efficiently use these datasets for policy optimization, we propose a novel value enhancement method to improve the performance of a given initial policy computed by existing state-of-the-art RL algorithms.