Co-occurring keywords · papers mentioning both
Papers
Optimal Non-Asymptotic Rates of Value Iteration for Average-Reward Markov Decision Processes
ICLR 2025
Provably and Practically Efficient Adversarial Imitation Learning with General Function Approximation
NeurIPS 2024
Symmetric Q-learning: Reducing Skewness of Bellman Error in Online Reinforcement Learning
AAAI 2024
Bi-Level Offline Policy Optimization with Limited Exploration
NeurIPS 2023
Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning
NeurIPS 2023
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning
ICLR 2023
Model-Bellman Inconsistency for Model-based Offline Reinforcement Learning
ICML 2023
Revisiting Bellman Errors for Offline Model Selection
ICML 2023
Toward Efficient Gradient-Based Value Estimation
ICML 2023
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning
JMLR 2022
On Instrumental Variable Regression for Deep Offline Policy Evaluation
JMLR 2022
Oracle Inequalities for Model Selection in Offline Reinforcement Learning
NeurIPS 2022
Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error
ICML 2022
Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters
NeurIPS 2022
Logistic Q-Learning
AISTATS 2021
On Uninformative Optimal Policies in Adaptive LQR with Unknown B-Matrix
L4DC 2021
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection
NeurIPS 2021
Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning
ICML 2021
Learning Near Optimal Policies with Low Inherent Bellman Error
ICML 2020
Minimax Weight and Q-Function Learning for Off-Policy Evaluation
ICML 2020
Provably Efficient Neural GTD for Off-Policy Learning
NeurIPS 2020
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration
NeurIPS 2020
Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison
UAI 2020
Neural Temporal-Difference Learning Converges to Global Optima
NeurIPS 2019
Basis refinement strategies for linear value function approximation in MDPs
NeurIPS 2015