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Deep RL

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Papers

Building Decision Tree for Imbalanced Classification via Deep Reinforcement Learning ACML 2021
On the Effect of Auxiliary Tasks on Representation Dynamics AISTATS 2021
Online Sparse Reinforcement Learning AISTATS 2021
A Contraction Approach to Model-based Reinforcement Learning AISTATS 2021
Sample Complexity Bounds for Two Timescale Value-based Reinforcement Learning Algorithms AISTATS 2021
Abstract Value Iteration for Hierarchical Reinforcement Learning AISTATS 2021
Provably Safe PAC-MDP Exploration Using Analogies AISTATS 2021
Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning AISTATS 2021
Q-learning with Logarithmic Regret AISTATS 2021
Minimax Model Learning AISTATS 2021
Reinforcement Learning in Parametric MDPs with Exponential Families AISTATS 2021
Reinforcement Learning for Constrained Markov Decision Processes AISTATS 2021
Provably Efficient Actor-Critic for Risk-Sensitive and Robust Adversarial RL: A Linear-Quadratic Case AISTATS 2021
Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning AISTATS 2021
Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation AISTATS 2021
Provably Efficient Safe Exploration via Primal-Dual Policy Optimization AISTATS 2021
Online Model Selection for Reinforcement Learning with Function Approximation AISTATS 2021
No-Regret Reinforcement Learning with Heavy-Tailed Rewards AISTATS 2021
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces AISTATS 2021
Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited ALT 2021
Adaptive Reward-Free Exploration ALT 2021
Online Markov Decision Processes with Aggregate Bandit Feedback COLT 2021
On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning COLT 2021
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective COLT 2021
Non-stationary Reinforcement Learning without Prior Knowledge: an Optimal Black-box Approach COLT 2021