Papers › Health-Informed Policy Gradients for Multi-Agent Reinforcement Learning

Health-Informed Policy Gradients for Multi-Agent Reinforcement Learning

2 Aug 2019arXiv:1908.01022archive 2025-07-28

Ross E. Allen, Jayesh K. Gupta, Jaime Pena, Yutai Zhou, Javona White Bear, Mykel J. Kochenderfer

This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function. We use this definition as a credit assignment term in a policy gradient algorithm to distinguish the contributions of individual agents to the global reward. The health-informed credit assignment is then extended to a multi-agent variant of the proximal policy optimization algorithm and demonstrated on particle and multiwalker robot environments that have characteristics such as system health, risk-taking, semi-expendable agents, continuous action spaces, and partial observability. We show significant improvement in learning performance compared to policy gradient methods that do not perform multi-agent credit assignment.

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Multi-agent Reinforcement Learning Policy Gradient Methods Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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