DACOOP-A
Decentralized Adaptive Cooperative Pursuit via Attention
Zheng Zhang (Sun Yat-sen University)
Dengyu Zhang (Sun Yat-sen University)
Qingrui Zhang (Sun Yat-sen University)
W. Pan (TU Delft - Robot Dynamics, The University of Manchester)
Tianjiang Hu (Sun Yat-sen University)
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Abstract
Integrating rule-based policies into reinforcement learning promises to improve data efficiency and generalization in cooperative pursuit problems. However, most implementations do not properly distinguish the influence of neighboring robots in observation embedding or inter-robot interaction rules, leading to information loss and inefficient cooperation. This letter proposes a cooperative pursuit algorithm named Decentralized Adaptive COOperative Pursuit via Attention (DACOOP-A) by empowering reinforcement learning with artificial potential field and attention mechanisms. An attention-based framework is developed to emphasize important neighbors by concurrently integrating the learned attention scores into observation embedding and inter-robot interaction rules. A KL divergence regularization is introduced to alleviate the resultant learning stability issue. Improvements in data efficiency and generalization are demonstrated through numerical simulations. Extensive quantitative analyses are performed to illustrate the advantages of the proposed modules. Real-world experiments are performed to justify the feasibility of DACOOP-A in physical systems.