AIBullisharXiv – CS AI · Mar 57/10
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HALyPO: Heterogeneous-Agent Lyapunov Policy Optimization for Human-Robot Collaboration
Researchers developed HALyPO (Heterogeneous-Agent Lyapunov Policy Optimization), a new approach to improve stability in human-robot collaboration through multi-agent reinforcement learning. The method addresses the 'rationality gap' between human and robot learning by using Lyapunov stability conditions to prevent policy oscillations and divergence during training.