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Found 7 papers, 2 papers with code
Near Minimax-Optimal Distributional Temporal Difference Algorithms and The Freedman Inequality in Hilbert Spaces
In the tabular case, \citet{rowland2018analysis} and \citet{rowland2023analysis} proved the asymptotic convergence of two instances of distributional TD, namely categorical temporal difference algorithm (CTD) and quantile temporal difference algorithm (QTD), respectively.
Estimation and Inference in Distributional Reinforcement Learning
This implies the distributional policy evaluation problem can be solved with sample efficiency.
Distributional Reinforcement Learning
reinforcement-learning
Semi-Infinitely Constrained Markov Decision Processes and Efficient Reinforcement Learning
To the best of our knowledge, we are the first to apply tools from semi-infinitely programming (SIP) to solve constrained reinforcement learning problems.
Statistical Estimation of Confounded Linear MDPs: An Instrumental Variable Approach
Specifically, we propose a two-stage estimator based on the instrumental variables and establish its statistical properties in the confounded MDPs with a linear structure.
Towards Theoretical Understandings of Robust Markov Decision Processes: Sample Complexity and Asymptotics
In this paper, we study the non-asymptotic and asymptotic performances of the optimal robust policy and value function of robust Markov Decision Processes(MDPs), where the optimal robust policy and value function are solved only from a generative model.
Intervention Generative Adversarial Nets
In this paper we propose a novel approach for stabilizing the training process of Generative Adversarial Networks as well as alleviating the mode collapse problem.
Intervention Generative Adversarial Networks
In this paper we propose a novel approach for stabilizing the training process of Generative Adversarial Networks as well as alleviating the mode collapse problem.
