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Reinforcement Learning with Lookahead Information
In reinforcement learning (RL), agents sequentially interact with a changing environment, aiming to collect as much reward as possible.
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Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback
The standard assumption in reinforcement learning (RL) is that agents observe feedback for their actions immediately.
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