TL;DR
Experiments on the StarCraft Multi-Agent Challenge benchmark demonstrate that CLRE achieves superior win rates and sample efficiency compared with state-of-the-art MARL methods, showing that the proposed approach enables diverse and efficient cooperation among agents in complex environments.
Fair Multi-Level Preference Optimization (Fair-MPO or $\Phi$-MPO), a new preference optimization framework for agentic learning, is proposed and a Fair Multi-Level Objective that addresses imbalance in agentic learning is introduced.
In complex, partially observable environments such as real-time strategy games, relying on decentralized multi-agent systems to learn efficient cooperative strategies is a challenging task. This paper proposes a Role Cognition Decomposition algorithm based on Multi-Agent Reinforcement Learning (RCD-MARL), which combine...
Multi-agent reinforcement learning (MARL) is impeded by the combinatorial explosion of joint action spaces and the neglect of complex inter-agent dependencies, which severely hinder effective coordination and accurate credit as signment. To address these challenges, this paper proposes a novel framework termed Lightwei...
Many cooperative multi‐agent tasks are naturally defined by graph‐structured objectives, where agents must collectively reach, for example, a desired relational configuration or satisfy a set of constraints. These objectives often encode spatial arrangements, inter‐agent relations, or constraints that can be formal...
River, a simple training recipe that improves reward quality by filtering low-quality environments and augmenting outcome rewards with process-level behavior regularization is proposed, which achieves the best performance among evaluated open-source RL-trained 8B models across four terminal-agent benchmarks.
Efficient cooperation is challenging due to the usual partial observability of each agent in multi-agent reinforcement learning. Recurrent networks encode local interaction histories, but their hidden representations provide limited insight into the information underlying individual decisions. To address these challeng...
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