Reinforcement Learning

Online Sparse Reinforcement Learning AISTATS 2021 On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations NeurIPS 2021 On-Policy Deep Reinforcement Learning for the Average-Reward Criterion ICML 2021 On Proximal Policy Optimization’s Heavy-tailed Gradients ICML 2021 On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function COLT 2021 On Reinforcement Learning with Adversarial Corruption and Its Application to Block MDP ICML 2021 On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game ICML 2021 On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method NeurIPS 2021 On the Effect of Auxiliary Tasks on Representation Dynamics AISTATS 2021 On the Estimation Bias in Double Q-Learning NeurIPS 2021 On the Expressivity of Markov Reward NeurIPS 2021 On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning AISTATS 2021 On the Linear Convergence of Policy Gradient Methods for Finite MDPs AISTATS 2021 On the Model-Based Stochastic Value Gradient for Continuous Reinforcement Learning L4DC 2021 On the Optimality of Batch Policy Optimization Algorithms ICML 2021 On the role of planning in model-based deep reinforcement learning ICLR 2021 On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning COLT 2021 On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift JMLR 2021 On the Theory of Reinforcement Learning with Once-per-Episode Feedback NeurIPS 2021 OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning ICLR 2021 OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation ICML 2021 Optimal Cost Design for Model Predictive Control L4DC 2021 Optimal Off-Policy Evaluation from Multiple Logging Policies ICML 2021 Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings NeurIPS 2021 Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation WACV 2021