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Independent learning outperforms centralized methods in embodied MARL

By PulseAugur Editorial · [1 sources] ·

A new research paper titled "Embodiment-Induced Coordination Regimes in Tabular Multi-Agent Q-Learning" explores the effectiveness of different coordination strategies in multi-agent reinforcement learning. The study found that fully independent learning (IQL-IQL) outperformed fully centralized learning (CQL-CQL) in a predator-prey gridworld scenario, even with constraints on agent speed and stamina. The researchers propose a phenomenon called "temporal synchronization lock" where a shared value function can hinder learning by forcing capable agents to wait for limited partners, suggesting that centralized coordination is not universally beneficial and may underperform independent learning in embodied scenarios. AI

IMPACT Challenges the assumption that centralized coordination is always superior in multi-agent reinforcement learning, suggesting implications for deep MARL methods.

RANK_REASON The cluster contains a single academic paper detailing a new finding in multi-agent reinforcement learning. [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 →

Independent learning outperforms centralized methods in embodied MARL

COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Muhammad Ahmed Atif, Nehal Naeem Haji, Mohammad Shahid Shaikh, Muhammad Ebad Atif ·

    Embodiment-Induced Coordination Regimes in Tabular Multi-Agent Q-Learning

    arXiv:2601.17454v2 Announce Type: replace-cross Abstract: Centralized value learning underlies a broad class of multi-agent reinforcement learning methods, but its claimed advantage is typically evaluated in settings that confound coordination structure with function approximatio…

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