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Achieving Collective Welfare in Multi-Agent Reinforcement Learning via Suggestion Sharing
Yue Jin, Shuangqing Wei, Giovanni Montana
In human society, the conflict between self-interest and collective well-being often obstructs efforts to achieve shared welfare. Related concepts like the Tragedy of the Commons a…
Learning on One Mode: Addressing Multi-modality in Offline Reinforcement Learning
Mianchu Wang, Yue Jin, Giovanni Montana
Offline reinforcement learning (RL) seeks to learn optimal policies from static datasets without interacting with the environment. A common challenge is handling multi-modal action…
Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning
Ting Zhu, Yue Jin, Jeremie Houssineau +1
In decentralized multi-agent reinforcement learning, agents learning in isolation can lead to relative over-generalization (RO), where optimal joint actions are undervalued in favo…
State-Constrained Offline Reinforcement Learning
Charles A. Hepburn, Yue Jin, Giovanni Montana
Traditional offline reinforcement learning (RL) methods predominantly operate in a batch-constrained setting. This confines the algorithms to a specific state-action distribution p…