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

2025-05-02

AI Summary

In parallel reinforcement learning, homogeneous agents suffer from redundant state-space sampling, fundamentally limiting the acceleration ceiling of data collection. To address this, we propose a Collaborative Entropy Maximization (CEM) framework—the first to introduce the maximum-state-entropy principle into parallel RL—jointly optimizing individual exploratory behavior and inter-agent policy diversity. Our method employs centralized policy gradients, state-entropy regularization, and explicit diversity constraints. Moreover, we establish the first theoretical convergence-rate analysis tailored to specialized parallel sampling distributions. Empirical evaluation across multiple benchmark tasks demonstrates that CEM significantly reduces sampling redundancy, consistently outperforms homogeneous-agent baselines, and improves the downstream performance of batch RL algorithms.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Multiagent Learning
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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: extit{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.

Problem

• Specializing parallel agents' policies to exceed N-factor acceleration
• Maximizing entropy of collected data in parallel RL settings
• Balancing individual agent entropy with inter-agent diversity

Innovation

• Maximizes entropy of parallel collected data
• Balances individual and inter-agent diversity
• Uses centralized policy gradient method