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New MARL approach uses joint history estimators for N-agent ad hoc teamwork

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new methods for centralized critics in multi-agent reinforcement learning (MARL) specifically for N-agent ad hoc teamwork. This approach addresses scenarios where a dynamic set of learning agents must coordinate with unknown, uncontrolled teammates. The study compares controlled history estimators, which use only the learner's agent histories, with joint history estimators that also incorporate uncontrolled teammates' histories. Theoretical analysis and empirical results suggest that joint history estimators offer lower variance and improved performance, recommending their use for better sample efficiency and final returns in N-agent ad hoc teamwork tasks. AI

IMPACT This research could lead to more effective coordination in complex multi-agent systems, impacting areas like robotics and game AI.

RANK_REASON Research paper detailing a novel method in multi-agent reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New MARL approach uses joint history estimators for N-agent ad hoc teamwork

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Research paper detailing a novel method in multi-agent reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Andrew McAlinden, Caroline Wang, Steven Shi, Arrasy Rahman, Elad Liebman, Peter Stone ·

    Centralized Critics for N-Agent Ad Hoc Teamwork

    arXiv:2610.34799v1 Announce Type: new Abstract: Centralized training with decentralized execution (CTDE) has become the dominant paradigm in multi-agent reinforcement learning (MARL), motivating extensive study of what privileged information should be made available to the critic…

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