
Nearly Minimax Optimal Regret for Learning Infinitehorizon Averagereward MDPs with Linear Function Approximation
We study reinforcement learning in an infinitehorizon averagereward se...
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Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes
We study reinforcement learning (RL) with linear function approximation ...
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Learning Adversarial MDPs with Bandit Feedback and Unknown Transition
We consider the problem of learning in episodic finitehorizon Markov de...
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Learning Adversarial Markov Decision Processes with Delayed Feedback
Reinforcement learning typically assumes that the agent observes feedbac...
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Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection
We study the role of the representation of stateaction value functions ...
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Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints
We study reinforcement learning (RL) with linear function approximation ...
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Safe Reinforcement Learning with Linear Function Approximation
Safety in reinforcement learning has become increasingly important in re...
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Nearly Optimal Regret for Learning Adversarial MDPs with Linear Function Approximation
We study the reinforcement learning for finitehorizon episodic Markov decision processes with adversarial reward and full information feedback, where the unknown transition probability function is a linear function of a given feature mapping. We propose an optimistic policy optimization algorithm with Bernstein bonus and show that it can achieve Õ(dH√(T)) regret, where H is the length of the episode, T is the number of interaction with the MDP and d is the dimension of the feature mapping. Furthermore, we also prove a matching lower bound of Ω̃(dH√(T)) up to logarithmic factors. To the best of our knowledge, this is the first computationally efficient, nearly minimax optimal algorithm for adversarial Markov decision processes with linear function approximation.
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