Multi-Agent Reinforcement Learning (MARL)

Multi-Agent Reinforcement Learning (MARL) studies learning and coordination among multiple autonomous agents sharing an environment. It addresses non-stationarity, scalability and coordination challenges via cooperative, competitive or mixed reward structures. MARL is applied in simulations, distributed control and multi-agent decision-making for complex dyn…

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MARL describes learning and coordination methods for multiple autonomous agents in shared environments with cooperative or competitive objectives.

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Wikipedia: Multi-agent reinforcement learning Ray RLlib documentation: Multi-Agent Training PettingZoo GitHub (Multi-Agent Environments)

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Reinforcement Learning

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