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Dreamer-CPC enhances MARL with historical message learning · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced Dreamer-CPC, a novel decentralized multi-agent reinforcement learning (MARL) method that enhances communication by integrating Collective Predictive Coding (CPC) with the DreamerV3 world model. This approach allows agents to learn and exchange messages reflecting historical observations and actions, rather than just current ones. Evaluations in the Observer and CatchApple environments demonstrated Dreamer-CPC's superiority over existing methods, particularly in CatchApple where it achieved 4 to 5 times the episode return, highlighting its effectiveness in coordinated decision-making under conditions of missing observations. AI

IMPACT This research could improve coordination in decentralized AI systems, especially in scenarios with incomplete or delayed information.

RANK_REASON The cluster contains an academic paper detailing a new method for multi-agent reinforcement learning.

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Dreamer-CPC enhances MARL with historical message learning · 2 sources tracked

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The cluster contains an academic paper detailing a new method for multi-agent reinforcement learning.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Taisuke Takayama, Naoto Yoshida, Tadahiro Taniguchi ·

    Dreamer-CPC: Message Learning with World Models for Decentralized Multi-agent Reinforcement Learning

    arXiv:2607.19809v1 Announce Type: cross Abstract: In multi-agent reinforcement learning (MARL), inter-agent communication is effective for improving performance under partial observability. Representation learning-based approaches enable decentralized agents to learn messages gro…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Tadahiro Taniguchi ·

    Dreamer-CPC: Message Learning with World Models for Decentralized Multi-agent Reinforcement Learning

    In multi-agent reinforcement learning (MARL), inter-agent communication is effective for improving performance under partial observability. Representation learning-based approaches enable decentralized agents to learn messages grounded in their own observations, but they rely onl…

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