Representation Learning

TIMAR: Transition-Informed Representation for Sample-Efficient Multi-agent Reinforcement Learning
In MARL (Multi-Agent Reinforcement Learning), the trial-and-error learning paradigm based on multiple agents requires massive …
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Adaptive Pessimism via Target Q-Value for Offline Reinforcement Learning
Offline reinforcement learning (RL) methods learn from datasets without further environment interaction, facing errors due to …
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