Enhancing Diversity in Parallel Agents: A Maximum State Entropy Exploration Story (2505.01336v2)

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.

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

The paper "Enhancing Diversity in Parallel Agents: A Maximum State Entropy Exploration Story" introduces a novel framework for exploration in Reinforcement Learning (RL), focusing on maximizing state entropy in parallel settings. This research highlights the advantages of utilizing multiple agents exploring in parallel, as opposed to scaling interactions purely through identical agents replicating the same environment policies.

Conceptual Overview

Parallel data collection in RL has enabled significant advancements by reducing the time needed to gather data. Traditionally, several identical agents interacting with distinct replicas of an environment have provided a straightforward means of achieving efficiency gains. However, this paper posits that there is untapped potential in diversifying the policies of parallel agents, leading to more than just linear speedups.

The authors propose a learning framework to optimize the entropy of the data collected in parallel settings. By balancing the entropy within individual agents and promoting diversity between agents, the framework potentially minimizes redundant explorations across the network of agents. This approach is implemented through a centralized policy gradient strategy which harmonizes single-agent exploration with inter-agent diversity.

Theoretical Contributions

A significant part of this paper is devoted to demonstrating how parallel agent setups can lead to faster convergence in terms of entropy stabilization. The authors provide concentration bounds suggesting that the empirical mixture of state distributions achieves entropy stabilization at a faster rate than singular, non-parallel implementations. Key findings include:

• The paper mathematically articulates that a diversified parallel policy mixture achieves quicker stabilization due to reduced redundancy and increased state coverage.
• Proof structures leverage concentration inequalities to establish that agents following diverse policies contribute significantly to sample-efficient exploration, highlighting variance reduction advantages that parallel exploration provides.

Practical Implications and Validation

Empirical validation shows the proposed framework significantly enhances exploration efficiency across several benchmark grid-world environments, both deterministic and stochastic. Key performance metrics include normalized state entropy and entropy support size—two indicators reflecting the diversity and coverage effectiveness of the exploration strategies employed by the agents.

The results demonstrate that parallel exploration not only expedites learning but consistently outperforms single-agent baselines that demand equivalent interaction volumes. Additionally, the paper explores the utility of parallel exploration-generated datasets in offline reinforcement learning tasks, showcasing improvements in learning outcomes derived from enhanced state-space coverage.

Future Directions

This paper opens avenues for further research into optimizing exploration strategies using parallel agents in more complex and high-dimensional environments beyond grid-world scenarios. Future developments could focus on adapting these principles for continuous state-action spaces and integrating similar entropy maximization strategies into hierarchical or multi-task reinforcement learning paradigms.

The practical applications of this approach extend into robotics, where parallel RL could be leveraged for developing versatile, adaptive control policies within simulators before deployment in real-world systems. The introduction of diversity-promoting techniques for exploration not only enhances learning efficiency but also mitigates safety concerns during exploration by preventing redundant and potentially unsafe explorations.

Overall, the work lays a foundation for reconsidering exploration strategies in RL, particularly in contexts where computational resources allow scalable implementations and where model diversity can play a pivotal role in optimizing learning dynamics.