Copertina

Efficient Multi-Policy Evaluation for Reinforcement Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Liu, Shuze Daniel, Chen, Claire, Zhang, Shangtong
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Efficient Policy Evaluation with Safety Constraint for Reinforcement Learning
di: Chen, Claire, et al.
Pubblicazione: (2024)

Doubly Optimal Policy Evaluation for Reinforcement Learning
di: Liu, Shuze Daniel, et al.
Pubblicazione: (2024)

Efficient Policy Evaluation with Offline Data Informed Behavior Policy Design
di: Liu, Shuze, et al.
Pubblicazione: (2023)

Convergence of Two-Timescale Markovian Stochastic Approximations with Applications in Reinforcement Learning
di: Mahadevan, Vagul, et al.
Pubblicazione: (2026)

The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise
di: Liu, Shuze Daniel, et al.
Pubblicazione: (2024)

Beyond Linear Attention: Softmax Transformers Implement In-Context Reinforcement Learning
di: Xie, Zixuan, et al.
Pubblicazione: (2026)

Predicting Plasticity in Deep Continual Learning: A Theoretical Perspective
di: Wang, Jiuqi, et al.
Pubblicazione: (2026)

Towards Formalizing Reinforcement Learning Theory
di: Zhang, Shangtong
Pubblicazione: (2025)

MathlibLemma: Folklore Lemma Generation and Benchmark for Formal Mathematics
di: Liu, Xinyu, et al.
Pubblicazione: (2026)

Group Fairness in Multi-Task Reinforcement Learning
di: Song, Kefan, et al.
Pubblicazione: (2025)

Extensions of Robbins-Siegmund Theorem with Applications in Reinforcement Learning
di: Liu, Xinyu, et al.
Pubblicazione: (2025)

Towards Provable Emergence of In-Context Reinforcement Learning
di: Wang, Jiuqi, et al.
Pubblicazione: (2025)

Convergence and Emergence of In-Context Reinforcement Learning with Chain of Thought
di: Xie, Zixuan, et al.
Pubblicazione: (2026)

Multi-agent Markov Entanglement
di: Chen, Shuze, et al.
Pubblicazione: (2025)

Counterfactual Explanations for Continuous Action Reinforcement Learning
di: Dong, Shuyang, et al.
Pubblicazione: (2025)

Almost Sure Convergence Rates of Stochastic Approximation and Reinforcement Learning via a Poisson-Moreau Drift
di: Liu, Xinyu, et al.
Pubblicazione: (2026)

CRASH: Challenging Reinforcement-Learning Based Adversarial Scenarios For Safety Hardening
di: Kulkarni, Amar, et al.
Pubblicazione: (2024)

Transformers Can Learn Temporal Difference Methods for In-Context Reinforcement Learning
di: Wang, Jiuqi, et al.
Pubblicazione: (2024)

Adaptive Policy Selection and Fine-Tuning under Interaction Budgets for Offline-to-Online Reinforcement Learning
di: Bozkurt, Alper Kamil, et al.
Pubblicazione: (2026)

Almost Sure Convergence Rates and Concentration of Stochastic Approximation and Reinforcement Learning with Markovian Noise
di: Qian, Xiaochi, et al.
Pubblicazione: (2024)

Revisiting a Design Choice in Gradient Temporal Difference Learning
di: Qian, Xiaochi, et al.
Pubblicazione: (2023)

On the Divergence of Differential Temporal Difference Learning without Local Clocks
di: Antrobius, David, et al.
Pubblicazione: (2026)

Global Optimality and Finite Sample Analysis of Softmax Off-Policy Actor Critic under State Distribution Mismatch
di: Zhang, Shangtong, et al.
Pubblicazione: (2021)

Online Estimation and Inference for Robust Policy Evaluation in Reinforcement Learning
di: Liu, Weidong, et al.
Pubblicazione: (2023)

Almost Sure Convergence of Linear Temporal Difference Learning with Arbitrary Features
di: Wang, Jiuqi, et al.
Pubblicazione: (2024)

Offline Two-Player Zero-Sum Markov Games with KL Regularization
di: Chen, Claire, et al.
Pubblicazione: (2026)

A Survey of In-Context Reinforcement Learning
di: Moeini, Amir, et al.
Pubblicazione: (2025)

Safe In-Context Reinforcement Learning
di: Moeini, Amir, et al.
Pubblicazione: (2025)

Linear $Q$-Learning Does Not Diverge in $L^2$: Convergence Rates to a Bounded Set
di: Liu, Xinyu, et al.
Pubblicazione: (2025)

Efficient Online Reinforcement Learning for Diffusion Policy
di: Ma, Haitong, et al.
Pubblicazione: (2025)

Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning
di: Qiu, Shuang, et al.
Pubblicazione: (2024)

Finite Sample Analysis of Linear Temporal Difference Learning with Arbitrary Features
di: Xie, Zixuan, et al.
Pubblicazione: (2025)

Almost Sure Convergence of Differential Temporal Difference Learning for Average Reward Markov Decision Processes
di: Blaser, Ethan, et al.
Pubblicazione: (2026)

Sample-Efficient Policy Constraint Offline Deep Reinforcement Learning based on Sample Filtering
di: Chen, Yuanhao, et al.
Pubblicazione: (2025)

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance
di: He, Jinmin, et al.
Pubblicazione: (2025)

Asymptotic and Finite Sample Analysis of Nonexpansive Stochastic Approximations with Markovian Noise
di: Blaser, Ethan, et al.
Pubblicazione: (2024)

Experience Replay Addresses Loss of Plasticity in Continual Learning
di: Wang, Jiuqi, et al.
Pubblicazione: (2025)

Mildly Constrained Evaluation Policy for Offline Reinforcement Learning
di: Xu, Linjie, et al.
Pubblicazione: (2023)

Distorted Distributional Policy Evaluation for Offline Reinforcement Learning
di: Iwaki, Ryo, et al.
Pubblicazione: (2026)

Toward Evaluating Robustness of Reinforcement Learning with Adversarial Policy
di: Zheng, Xiang, et al.
Pubblicazione: (2023)