Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games

Tailia Malloy; Tim Klinger; Miao Liu; Matthew Riemer; Gerald Tesauro; Chris R. Sims

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arXiv · 2011.11517

Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games

Tailia Malloy·Tim Klinger·Miao Liu·Matthew Riemer·Gerald Tesauro·Chris R. Sims

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Abstract

This paper introduces an information-theoretic constraint on learned policy complexity in the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) reinforcement learning algorithm. Previous research with a related approach in continuous control experiments suggests that this method favors learning policies that are more robust to changing environment dynamics. The multi-agent game setting naturally requires this type of robustness, as other agents' policies change throughout learning, introducing a nonstationary environment. For this reason, recent methods in continual learning are compared to our approach, termed Capacity-Limited MADDPG. Results from experimentation in multi-agent cooperative and competitive tasks demonstrate that the capacity-limited approach is a good candidate for improving learning performance in these environments.

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BibTeX RIS

Tailia Malloy, Tim Klinger, Miao Liu, Matthew Riemer, Gerald Tesauro, Chris R. Sims. 2020-11-23. Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games. https://arxiv.org/abs/2011.11517

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