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New research shows high entropy leads to symmetry equivariant policies in Dec-POMDPs

By PulseAugur Editorial · [1 sources] ·

A new paper explores how high entropy regularization can lead to symmetry-equivariant policies in Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs). The research demonstrates that sufficiently high entropy ensures policy gradient flow converges to a compatible joint policy across different initializations. Empirical tests on environments like Hanabi and Overcooked show that increasing the entropy coefficient significantly impacts cross-play returns, with potential for improvement by greedifying policies post-training. AI

IMPACT Suggests higher entropy coefficients for Dec-POMDP hyperparameter tuning, potentially improving multi-agent policy compatibility.

RANK_REASON This is a research paper published on arXiv detailing theoretical and empirical findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research shows high entropy leads to symmetry equivariant policies in Dec-POMDPs

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This is a research paper published on arXiv detailing theoretical and empirical findings. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Johannes Forkel, Constantin Ruhdorfer, Andreas Bulling, Jakob Foerster ·

    High entropy leads to symmetry equivariant policies in Dec-POMDPs

    arXiv:2511.22581v3 Announce Type: replace Abstract: We prove that in any Dec-POMDP, sufficiently high entropy regularization ensures that the policy gradient flow with tabular softmax parametrization always converges, for any initialization, to the same joint policy, and that thi…

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