Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2112.12458v3
• Date: Thu, 26 Oct 2023 11:11:26 GMT
• Title: Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning
• Authors: Rapha\"el Avalos, Mathieu Reymond, Ann Now\'e, Diederik M. Roijers
• Abstract summary: This paper presents a new type of reinforcement learning algorithm for cooperative partially observable environments. We use a dueling architecture to learn for each agent a decentralized best-response policies via individual advantage functions. Evaluation on the StarCraft II multi-agent challenge benchmark shows that LAN reaches state-of-the-art performance.
• Score: 1.1879716317856945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many recent successful off-policy multi-agent reinforcement learning (MARL) algorithms for cooperative partially observable environments focus on finding factorized value functions, leading to convoluted network structures. Building on the structure of independent Q-learners, our LAN algorithm takes a radically different approach, leveraging a dueling architecture to learn for each agent a decentralized best-response policies via individual advantage functions. The learning is stabilized by a centralized critic whose primary objective is to reduce the moving target problem of the individual advantages. The critic, whose network's size is independent of the number of agents, is cast aside after learning. Evaluation on the StarCraft II multi-agent challenge benchmark shows that LAN reaches state-of-the-art performance and is highly scalable with respect to the number of agents, opening up a promising alternative direction for MARL research.

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