Feb 3, 2024 · We propose MACE, a simple yet effective multi-agent coordinated exploration method. By communicating only local novelty, agents can take into account other ...

MACE introduces a novelty-based intrinsic reward and a hindsight-based intrinsic reward to enable coordinated ex- ploration in decentralized cooperative tasks.

Aug 10, 2024 · MACE introduces a novelty-based intrinsic reward and a hindsight-based intrinsic reward to enable coordinated exploration in decentralized ...

Oct 22, 2024 · By communicating only local novelty, agents can take into account other agents' local novelty to approximate the global novelty. Further, we ...

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Aug 12, 2024 · By sharing this local novelty, the agents can get a sense of the global novelty and coordinate their exploration. MACE also introduces a new way ...

[AAAI 2024] Settling Decentralized Multi-Agent Coordinated Exploration by Novelty Sharing ... Reproduction of self-play described in paper "Emergent Complexity ...

We investigate multi-agent cooperation from many aspects, including adaptive learning rates, reward sharing, roles, and fairness.

Aug 13, 2024 · Exploration in decentralized cooperative multi-agent reinforcement learning faces two challenges. One is that the novelty of global states ...

Stability of Multi-Agent Learning in Competitive Networks: Delaying the Onset of Chaos, ➖ ; Settling Decentralized Multi-Agent Coordinated Exploration by Novelty ...