Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Offline Actor-Critic for Average Reward MDPs
Authors: William Powell, Jeongyeol Kwon, Qiaomin Xie, Hanbaek Lyu
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 28,947 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,418 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper does not include experiments. |
| Researcher Affiliation | Academia | William Powell Department of Mathematics University of Wisconsin-Madison Madison, WI 53706 EMAIL Jeongyeol Kwon Wisconsin Institute for Discovery Madison, WI 53706 EMAIL Qiaomin Xie Department of Industrial and Systems Engineering University of Wisconsin-Madison Madison, WI 53706 EMAIL Hanbaek Lyu Department of Mathematics University of Wisconsin-Madison Madison, WI 53706 EMAIL |
| Pseudocode | Yes | Algorithm 1 Average Reward Actor-Critic 1: Input: D (dataset), Bw (function class parameter), β (uncertainty parameter), η (stepsize) 2: Form empirical covariance : ˆΛ I + PN i=1 ϕ(si, ai)ϕ(si, ai) 3: Initialize: θ1 = 0 4: for k = 1, . . . , K do 5: Let (wk, ξk, Jk) solve (6) 6: Update policy parameter: θk+1 θk + ηwk. 7: end for 8: Output: πout = Unif[π1, . . . , πK]. |
| Open Source Code | No | Answer: [NA] Justification: The paper does not include experiments requiring code. |
| Open Datasets | No | Answer: [NA] Justification: The paper does not include experiments requiring code. |
| Dataset Splits | No | Answer: [NA] Justification: The paper does not include experiments. |
| Hardware Specification | No | Answer: [NA] Justification: The paper does not include experiments. |
| Software Dependencies | No | Answer: [NA] Justification: The paper does not include experiments. |
| Experiment Setup | No | Answer: [NA] Justification: The paper does not include experiments. |