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Reinforcing the World's Edge: A Continual Learning Problem in the Multi-Agent-World Boundary
Ordinary decentralized multi-agent reinforcement learning presents each focal agent with a continual learning problem: peer updates change its induced rewards and dynamics even when the joint Markov game is stationary. We connect the lifetime of success-conditioned reusable structure to peer learnin
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