Reinforcement Learning in Large Multi-agent Systems
Adrian Agogino, Kagan Tumer · 2005
Enabling reinforcement learning to be eective in large-scale multi-agent Markov Decisions Problems is a challenging task. To address this problem we propose a multi-agent variant of Q-learning: “Q Updates with Immediate Counterfactual Rewards-learning” (QUICR-learning). Given a global reward function over all agents that the large-scale system is trying to maximize, QUICR-learning breaks down the global reward into many agent-specific rewards that have the following two properties: 1) agents maximizing their agentspecific rewards tend to maximize the global reward, 2) an agent’s action has a large influence on its agent-specific reward, allowing it to learn quickly. Each agent then uses standard Q-learning type updates to form a policy to maximize the agent-specific rewards. Results on multi-agent grid-world problems over two topologies, show that QUICRlearning can be eective with hundreds of agents and can achieve up to 300% improvements in performance over both conventional and local Q-learning in the largest tested systems.