Horizon-free Learning for Markov Decision Processes and Games: Stochastically Bounded Rewards and Improved Bounds

Shengshi Li, L. Yang
2/12/2026

No confident code implementation found for this paper. 2 possible matches can be shown below.

Semantic Scholar

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 2 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Integration-free Kernels for Equivariant Gaussian Process Modelling

93% match1 repo12026

Augmentation-free Dense Contrastive Distillation for Efficient Semantic Segmentation

93% match1 repo192026

Enhancing Knowledge Transfer for Task Incremental Learning with Data-free Subnetwork

93% match1 repo42026

PaintSeg: Painting Pixels for Training-free Segmentation

92% match1 repo142026

NICE: Neurogenesis Inspired Contextual Encoding for Replay-free Class Incremental Learning

92% match1 repo292026

Attack-free Evaluating and Enhancing Adversarial Robustness on Categorical Data

92% match2026

Cite this paper

@article{li2026horizonfree,
  title  = {Horizon-free Learning for Markov Decision Processes and Games: Stochastically Bounded Rewards and Improved Bounds},
  author = {Shengshi Li and L. Yang},
  year   = {2026},
  url    = {https://api.semanticscholar.org/CorpusID:10ad8a738e8c27d85ce8f848b726afaa79de080d},
  journal = {ICML 2023 2023}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/10ad8a738e8c27d85ce8f848b726afaa79de080d.svg)](https://codeofpaper.com/paper/10ad8a738e8c27d85ce8f848b726afaa79de080d)

Discussion