Learning Efficient Flocking Control based on Gibbs Random Fields

• URL: http://arxiv.org/abs/2502.02984v1
• Date: Wed, 05 Feb 2025 08:27:58 GMT
• Title: Learning Efficient Flocking Control based on Gibbs Random Fields
• Authors: Dengyu Zhang, Chenghao, Feng Xue, Qingrui Zhang,
• Abstract summary: Multi-agent reinforcement learning framework built on Gibbs Random Fields (GRFs) An action attention module is introduced to implicitly anticipate the motion intentions of neighboring robots. Proposed framework enables learning an efficient distributed control policy for multi-robot systems in challenging environments with success rate around $99%$.
• Score: 8.715391538937707
• License:
• Abstract: Flocking control is essential for multi-robot systems in diverse applications, yet achieving efficient flocking in congested environments poses challenges regarding computation burdens, performance optimality, and motion safety. This paper addresses these challenges through a multi-agent reinforcement learning (MARL) framework built on Gibbs Random Fields (GRFs). With GRFs, a multi-robot system is represented by a set of random variables conforming to a joint probability distribution, thus offering a fresh perspective on flocking reward design. A decentralized training and execution mechanism, which enhances the scalability of MARL concerning robot quantity, is realized using a GRF-based credit assignment method. An action attention module is introduced to implicitly anticipate the motion intentions of neighboring robots, consequently mitigating potential non-stationarity issues in MARL. The proposed framework enables learning an efficient distributed control policy for multi-robot systems in challenging environments with success rate around $99\%$, as demonstrated through thorough comparisons with state-of-the-art solutions in simulations and experiments. Ablation studies are also performed to validate the efficiency of different framework modules.

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