Multi-agent reinforcement learning (MARL) is a subfield of machine learning where multiple agents interact within an environment to learn optimal behaviors through trial and error. This approach allows agents to not only learn from their own actions but also adapt their strategies based on the behaviors of other agents, fostering cooperative or competitive dynamics. In the context of artificial intelligence and machine learning, MARL provides a framework for solving complex decision-making problems that involve multiple autonomous entities.
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