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Independent Multi-Agent Reinforcement Learning with Counterfactual Semantic-Social World Models
Fully decentralized multi-agent reinforcement learning (MARL), also referred to as independent learning, requires each agent to learn and act using only its local information and experience, without a centralized critic or inter-agent communication. Such a stringent information structure renders the
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