Enhancing Diversity in Parallel Agents: A Maximum State Entropy Exploration Story

TL;DR

This paper introduces a maximum state entropy framework for parallel reinforcement learning agents, enhancing data diversity and efficiency by balancing individual and inter-agent policy diversity, supported by empirical results and theoretical analysis.

Contribution

It proposes a novel entropy-maximizing approach for parallel RL agents that improves data efficiency and diversity, with a centralized policy gradient method and theoretical insights.

Findings

Empirical improvements over identical agent systems

Synergy with batch RL techniques

Faster convergence rates for specialized sampling distributions

Abstract

Parallel data collection has redefined Reinforcement Learning (RL), unlocking unprecedented efficiency and powering breakthroughs in large-scale real-world applications. In this paradigm, $N$ identical agents operate in $N$ replicas of an environment simulator, accelerating data collection by a factor of $N$. A critical question arises: \textit{Does specializing the policies of the parallel agents hold the key to surpass the $N$ factor acceleration?} In this paper, we introduce a novel learning framework that maximizes the entropy of collected data in a parallel setting. Our approach carefully balances the entropy of individual agents with inter-agent diversity, effectively minimizing redundancies. The latter idea is implemented with a centralized policy gradient method, which shows promise when evaluated empirically against systems of identical agents, as well as synergy with batch RL techniques that can exploit data diversity. Finally, we provide an original concentration analysis that shows faster rates for specialized parallel sampling distributions, which supports our methodology and may be of independent interest.