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

Vincenzo De Paola, Riccardo Zamboni, Mirco Mutti, Marcello Restelli
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:12718-12738, 2025.

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: 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-de-paola25a, title = {Enhancing Diversity In Parallel Agents: A Maximum State Entropy Exploration Story}, author = {De Paola, Vincenzo and Zamboni, Riccardo and Mutti, Mirco and Restelli, Marcello}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {12718--12738}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/de-paola25a/de-paola25a.pdf}, url = {https://proceedings.mlr.press/v267/de-paola25a.html}, 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: 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.} }
Endnote
%0 Conference Paper %T Enhancing Diversity In Parallel Agents: A Maximum State Entropy Exploration Story %A Vincenzo De Paola %A Riccardo Zamboni %A Mirco Mutti %A Marcello Restelli %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-de-paola25a %I PMLR %P 12718--12738 %U https://proceedings.mlr.press/v267/de-paola25a.html %V 267 %X 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: 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.
APA
De Paola, V., Zamboni, R., Mutti, M. & Restelli, M.. (2025). Enhancing Diversity In Parallel Agents: A Maximum State Entropy Exploration Story. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:12718-12738 Available from https://proceedings.mlr.press/v267/de-paola25a.html.

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