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Chentai Qiao

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2 papers

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ECAI Conference 2025 Conference Paper

ADAPT: Auction-Based Dynamic Prioritization for Multi-Agent Coordination

Zaipeng Xie
Chentai Qiao
Nuo Yang
Yiming Zhao

Effective coordination in multi-agent systems remains challenging in dynamic and partially observable environments, where agents must reason over evolving interdependencies and limited communication bandwidth. We propose ADAPT, a unified framework for multi-agent coordination that integrates message compression, dependency estimation, and a novel auction-based dynamic prioritization mechanism. In ADAPT, agents exchange compact messages and compute dependency scores to determine how much their behavior depends on others. A distributed auction protocol then assigns priority positions, guiding autoregressive decision-making in a manner aligned with inter-agent influence. This enables flexible, influence-aware coordination without centralized control or extensive communication rounds. Experiments on SMACv2 and GRF show that ADAPT achieves higher win rates, faster convergence, and lower communication cost compared to state-of-the-art baselines. Further analyses confirm its scalability to large teams, compatibility with value decomposition, and runtime efficiency. These results show that ADAPT enables scalable, efficient, and modular multi-agent coordination.

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ECAI Conference 2023 Conference Paper

IPERS: Individual Prioritized Experience Replay with Subgoals for Sparse Reward Multi-Agent Reinforcement Learning

Zaipeng Xie
Yufeng Zhang
Chentai Qiao
Sitong Shen

Multi-agent reinforcement learning commonly uses a global team reward signal to represent overall collaborative performance. Value decomposition breaks this global reward into estimated individual value functions per agent, enabling efficient training. However, in sparse reward environments, agents struggle to assess if their actions achieve the team goal, slowing convergence. This impedes the algorithm’s convergence rate and overall efficacy. We present IPERS, an Individual Prioritized Experience Replay algorithm with Subgoals for Sparse Reward Multi-Agent Reinforcement Learning. IPERS integrates joint action decomposition and prioritized experience replay, maintaining invariance between global and individual loss gradients. Subgoals serve as intermediate goals that break down complex tasks into simpler steps with dense feedback and provide helpful intrinsic rewards that guide agents. This facilitates learning coordinated policies in challenging collaborative environments with sparse rewards. Experimental evaluations of IPERS in both the SMAC and GRF environments demonstrate rapid adaptation to diverse multi-agent tasks and significant improvements in win rate and convergence performance relative to state-of-the-art algorithms.

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