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

Tyler Malloy
Tyler Malloy
Matthew Riemer
Matthew Riemer
Chris R. Sims
Chris R. Sims
Cited by: 0|Bibtex|Views3
Other Links: arxiv.org

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 cha...More

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