On Gap-dependent Bounds for Offline Reinforcement Learning

Authors: Xinqi Wang, Qiwen Cui, Simon S. Du

NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical This paper presents a systematic study on gap-dependent sample complexity in offline reinforcement learning. ... Lastly, we present nearly-matching lower bounds to complement our gap-dependent upper bounds. ... 1.1 Main Contributions We present novel analyses for the standard VI-LCB algorithm (Algorithm 2). ... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A]
Researcher Affiliation Academia Xinqi Wang Institute for Interdisciplinary Information Sciences Tsinghua University wangxqkaxdd@gmail.com Qiwen Cui Paul G. Allen School of Computer Science Engineering University of Washington qwcui@cs.washington.edu Simon S. Du Paul G. Allen School of Computer Science Engineering University of Washington ssdu@cs.washington.edu
Pseudocode Yes Algorithm 1: VI-LCB ... Algorithm 2: Subsampled VI-LCB
Open Source Code No (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A]
Open Datasets No The paper is theoretical and does not involve empirical experiments, dataset usage, or training. The ethics checklist states: "(a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A]"
Dataset Splits No The paper is theoretical and does not involve empirical experiments or dataset splits for validation. No mention of validation splits.
Hardware Specification No The paper is theoretical and does not conduct experiments. The ethics checklist states: "(d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A]"
Software Dependencies No The paper is theoretical and does not conduct experiments. Therefore, it does not list software dependencies with version numbers.
Experiment Setup No The paper is theoretical and does not describe any experimental setup, hyperparameters, or training settings for empirical runs.