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Deep RL

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Papers

Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables AISTATS 2023
Byzantine-Robust Online and Offline Distributed Reinforcement Learning AISTATS 2023
Mode-constrained Model-based Reinforcement Learning via Gaussian Processes AISTATS 2023
A Finite Sample Complexity Bound for Distributionally Robust Q-learning AISTATS 2023
Provably Efficient Reinforcement Learning via Surprise Bound AISTATS 2023
Exploration in Reward Machines with Low Regret AISTATS 2023
Neural Simulated Annealing AISTATS 2023
Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation AISTATS 2023
Reinforcement Learning for Adaptive Mesh Refinement AISTATS 2023
Optimism and Delays in Episodic Reinforcement Learning AISTATS 2023
Provably Efficient Model-Free Algorithms for Non-stationary CMDPs AISTATS 2023
Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach AISTATS 2023
Model-Based Uncertainty in Value Functions AISTATS 2023
Representation Learning in Deep RL via Discrete Information Bottleneck AISTATS 2023
Krylov–Bellman boosting: Super-linear policy evaluation in general state spaces AISTATS 2023
Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learning AISTATS 2023
On The Convergence Of Policy Iteration-Based Reinforcement Learning With Monte Carlo Policy Evaluation AISTATS 2023
Near-Optimal Differentially Private Reinforcement Learning AISTATS 2023
Uniformly Conservative Exploration in Reinforcement Learning AISTATS 2023
Provable Safe Reinforcement Learning with Binary Feedback AISTATS 2023
Adversarial Online Multi-Task Reinforcement Learning ALT 2023
Optimistic PAC Reinforcement Learning: the Instance-Dependent View ALT 2023
A Meta-Reinforcement Learning Algorithm for Causal Discovery CLEAR 2023
VO$Q$L: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation COLT 2023
Active Coverage for PAC Reinforcement Learning COLT 2023