Span|Based Optimal Sample Complexity for Weakly Communicating ...

Span-Based Optimal Sample Complexity for Weakly Communicating ...

Abstract page for arXiv paper 2403.11477: Span-Based Optimal Sample Complexity for Weakly Communicating and General Average Reward MDPs.

Span-Based Optimal Sample Complexity for Weakly Communicating ...

TL;DR: We resolve the span-based sample complexity of weakly communicating average reward MDPs and initiate the study of general multichain MDPs ...

Span-Based Optimal Sample Complexity for Weakly Communicating ...

We further investigate sample complexity in general (non-weakly-communicating) average-reward MDPs. We argue a new transient time parameter 𝖡 𝖡 ...

Span-Based Optimal Sample Complexity for Weakly Communicating ...

Span-Based Optimal Sample Complexity for Weakly. Communicating and General Average Reward MDPs. Matthew Zurek. Department of Computer Sciences. University of ...

Span-Based Optimal Sample Complexity for Weakly Communicating ...

Span-Based Optimal Sample Complexity for Weakly Communicating and General Average Reward MDPs. @article{Zurek2024SpanBasedOS, title={Span ...

NeurIPS 2024 Span-Based Optimal Sample Complexity for Weakly ...

Registration Required. You must be logged in to view this content. Successful Page Load. NeurIPS uses cookies to remember that you are logged in.

2403.11477 - Span-Based Optimal Sample Complexity for Weakly ...

For weakly communicating MDPs, we establish the complexity bound $\tilde{O}(SA\frac{H}{\epsilon2})$, where $H$ is the span of the bias function ...

Sample Complexity for Weakly Communicating and General ...

The study focuses on the sample complexity of learning optimal policies in weakly communicating and general average reward Markov decision processes (MDPs).

Yudong Chen - cs.wisc.edu

Span-Based Optimal Sample Complexity for Weakly Communicating and General Average Reward MDPs. Matthew Zurek, Yudong Chen Neural Information Processing ...

Span-Based Optimal Sample Complexity for Average Reward MDPs

Stats. Our result establishes a complexity bound eO(SAHε^2). Samples suffice to learn an ε-optimal policy in weakly communicating MDPs under certain conditions.

Span-Based Optimal Sample Complexity for Weakly Communicating ...

Span-Based Optimal Sample Complexity for Weakly Communicating and General Average Reward MDPs. Matthew Zurek ,. Yudong Chen. MLcs.ITmath.ITmath.OCstat.MLPRGen ...

The Plug-in Approach for Average-Reward and Discounted MDPs

Specifically it achieves the optimal diameter- and mixing-based sample complexities ... optimal regret rate in an unknown weakly communicating ...

Learning Unknown Markov Decision Processes: A Thompson ...

An MDP is weakly communicating (or weak accessible) if its states can be partitioned into two subsets: in the first subset all states are transient under every ...

Model-free Reinforcement Learning in Infinite-horizon Average ...

Define sp(v∗) = maxs v∗(s) − mins v∗(s) to be the span of the value function, which is known to be bounded for weakly communicating MDPs. In particular ...

The Optimal Sample Complexity of PAC Learning

The algorithm achieving this new bound is also based on a majority vote of classifiers. ... Weak Convergence and Empirical Processes. Springer, 1996. 2. 14. Page ...

Primal-Dual π Learning: Sample Complexity and Sublinear Run ...

The results are the first to achieve optimal dependence in $T$ for weakly communicating MDPs, based on two new techniques based on better discounted ...

REGAL: A Regularization based Algorithm for Reinforcement ... - TTIC

We provide an algorithm that achieves the optimal regret rate in an unknown weakly communicating Markov Decision Process. (MDP). The algorithm proceeds in ...

REGAL Revisited: Regularized Reinforcement Learning for Weakly ...

... optimal MDPs, the MDP with the largest span ... Tewari, “Regal: A regularization based algorithm for reinforce- ment learning in weakly communicating mdps,” arXiv ...

Hardness in Markov Decision Processes: Theory and Practice

REGAL: a regularization based algorithm for reinforcement learning in weakly communicating MDPs. In Uncertainty in Artificial Intelligence: Proceedings of ...

Low Sample and Communication Complexities in Decentralized ...

Abstract—Network-consensus-based decentralized learning op- timization algorithms have attracted a significant amount of attention in recent years due to ...