AI Summary: Introduces MADDPG, a framework that stabilizes multi-agent reinforcement learning by using centralized training and decentralized execution, allowing agents to successfully navigate mixed cooperative and competitive environments.
We explore deep reinforcement learning methods for multi-agent domains. We begin by analyzing the difficulty of traditional algorithms in the multi-agent case: Q-learning is challenged by an inherent non-stationarity of the environment, while policy gradient suffers from a variance that increases as the number of agents grows. We present an adaptation of actor-critic methods that considers action policies of other agents and is able to successfully learn policies that require complex multi-agent coordination. Our Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm utilizes centralized training with decentralized execution, allowing agents to discover complex physical behaviors like cooperation, coordination, and deception.
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