Multi-agent reinforcement learning is a branch of artificial intelligence where multiple agents learn to make decisions through interactions in a shared environment, improving their performance based on the rewards received from their actions. This learning framework allows agents to adapt to complex tasks by collaborating or competing, making it particularly relevant for scenarios involving multiple objectives or tasks. Agents in this setting can develop strategies that take into account the actions of other agents, which leads to emergent behaviors and improved efficiency in solving problems.
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