Enhancing Reinforcement Learning Using Data and Structure

Abstract

Reinforcement learning is a powerful approach for training intelligent agents to make decisions in complex environments. However, these algorithms often struggle when faced with challenging scenarios, such as sparse reward feedback or environments with multiple learning agents. Additionally, the requirement for a large number of training samples makes real-world deployment difficult. In this work, we present novel reinforcement learning algorithms that leverage data and structure to enhance the learning process and overcome these challenges. Specifically, we introduce new RL algorithms that leverage demonstration data to guide learning in sparse reward environments, exploit structural similarity and data to accelerate learning in such environments, use structure to learn in environments with multiple agents, and learn from data distributed among multiple clients without sharing the data. The proposed algorithms show promising results and offer potential for practical applications in complex and diverse environments.