linear function approximation

Stabilizing Q-learning with Linear Architectures for Provable Efficient Learning ICML 2022 TensorPlan and the Few Actions Lower Bound for Planning in MDPs under Linear Realizability of Optimal Value Functions ALT 2022 Towards painless policy optimization for constrained MDPs UAI 2022 Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation COLT 2021 Decentralized Single-Timescale Actor-Critic on Zero-Sum Two-Player Stochastic Games ICML 2021 Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL ICML 2021 Exponential Lower Bounds for Planning in MDPs With Linearly-Realizable Optimal Action-Value Functions ALT 2021 Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity ICLR 2021 Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation AISTATS 2021 Logarithmic Regret for Reinforcement Learning with Linear Function Approximation ICML 2021 Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes COLT 2021 Offline Constrained Multi-Objective Reinforcement Learning via Pessimistic Dual Value Iteration NeurIPS 2021 Online learning in MDPs with linear function approximation and bandit feedback. NeurIPS 2021 On the Value of Interaction and Function Approximation in Imitation Learning NeurIPS 2021 Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses NeurIPS 2021 Preferential Temporal Difference Learning ICML 2021 Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping ICML 2021 Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints NeurIPS 2021 Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation NeurIPS 2021 Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach ICML 2021 Safe Reinforcement Learning with Linear Function Approximation ICML 2021 Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient ICML 2021 Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation NeurIPS 2021 Variance-Aware Off-Policy Evaluation with Linear Function Approximation NeurIPS 2021 What are the Statistical Limits of Offline RL with Linear Function Approximation? ICLR 2021