A Distributional Perspective on Value Function Factorization Methods for Multi-Agent Reinforcement Learning
Wei-Fang Sun, Cheng-Kuang Lee, Chun‐Yi Lee · 2021
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.