A Distributional Perspective on Value Function Factorization Methods for Multi-Agent Reinforcement Learning

Distributional reinforcement learning (RL) provides beneficial impacts for the single-agent domain. However, distributional RL methods are not directly compatible with value function factorization methods for multi-agent reinforcement learning. This work provides a distributional perspective on value function factorization, offering a solution for bridging the gap between distributional RL and value function factorization methods.

Authors

• Wei-Fang Sun
• Cheng-Kuang Lee
• Chun-Yi Lee

Keywords

• Reinforcement Learning
• Multi-Agent RL
• Distributional RL