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Variational Regret Bounds for Reinforcement Learning
We consider undiscounted reinforcement learning in Markov decision proce...
05/14/2019 ∙ by Pratik Gajane, et al. ∙
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Regret Bounds for Kernel-Based Reinforcement Learning
We consider the exploration-exploitation dilemma in finite-horizon reinf...
04/12/2020 ∙ by Omar Darwiche Domingues, et al. ∙
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Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism
We consider un-discounted reinforcement learning (RL) in Markov decision...
06/24/2020 ∙ by Wang Chi Cheung, et al. ∙
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Large Scale Markov Decision Processes with Changing Rewards
We consider Markov Decision Processes (MDPs) where the rewards are unkno...
05/25/2019 ∙ by Adrian Rivera Cardoso, et al. ∙
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Non-Stationary Markov Decision Processes a Worst-Case Approach using Model-Based Reinforcement Learning
This work tackles the problem of robust zero-shot planning in non-statio...
04/22/2019 ∙ by Erwan Lecarpentier, et al. ∙
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Optimizing for the Future in Non-Stationary MDPs
Most reinforcement learning methods are based upon the key assumption th...
05/17/2020 ∙ by Yash Chandak, et al. ∙
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Dynamic Regret of Policy Optimization in Non-stationary Environments
We consider reinforcement learning (RL) in episodic MDPs with adversaria...
06/30/2020 ∙ by Yingjie Fei, et al. ∙
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A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces
07/09/2020 ∙ by Omar Darwiche Domingues, et al. ∙
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In this work, we propose KeRNS: an algorithm for episodic reinforcement learning in non-stationary Markov Decision Processes (MDPs) whose state-action set is endowed with a metric. Using a non-parametric model of the MDP built with time-dependent kernels, we prove a regret bound that scales with the covering dimension of the state-action space and the total variation of the MDP with time, which quantifies its level of non-stationarity. Our method generalizes previous approaches based on sliding windows and exponential discounting used to handle changing environments. We further propose a practical implementation of KeRNS, we analyze its regret and validate it experimentally.
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