On sample-efficient offline reinforcement learning: Data diversity, posterior sampling and beyond

[PDF] arxiv.org

Offline multitask representation learning for reinforcement learning

H Ishfaq, T Nguyen-Tang, S Feng, R Arora… - arXiv preprint arXiv …, 2024 - arxiv.org
We study offline multitask representation learning in reinforcement learning (RL), where a
learner is provided with an offline dataset from different tasks that share a common …
被引用次数：2 相关文章 所有 2 个版本
[PDF] arxiv.org

Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning

HL Hsu, W Wang, M Pajic, P Xu - arXiv preprint arXiv:2404.10728, 2024 - arxiv.org
We present the first study on provably efficient randomized exploration in cooperative multi-
agent reinforcement learning (MARL). We propose a unified algorithm framework for …
被引用次数：1 相关文章 所有 2 个版本
[PDF] arxiv.org

The Role of Inherent Bellman Error in Offline Reinforcement Learning with Linear Function Approximation

N Golowich, A Moitra - arXiv preprint arXiv:2406.11686, 2024 - arxiv.org
In this paper, we study the offline RL problem with linear function approximation. Our main
structural assumption is that the MDP has low inherent Bellman error, which stipulates that …
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