Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning
Authors: Dake Zhang, Boxiang Lyu, Shuang Qiu, Mladen Kolar, Tong Zhang
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For completeness, we examine a variant of the Model Win MDP introduced in Thomas & Brunskill (2016) to verify theoretical findings. ... The suboptimality results are reported in Figure 1. We can see that with a larger K, the suboptimality goes to 0, which serves as simulation evidence for our algorithm. |
| Researcher Affiliation | Academia | 1University of Chicago, IL, USA 2Hong Kong University of Science and Technology, Hong Kong, China 3University of Southern California, CA, USA 4University of Illinois Urbana-Champaign, IL, USA |
| Pseudocode | Yes | Algorithm 1 RSPVI Algorithm ... Algorithm 2 VA-RSPVI Algorithm |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | For completeness, we examine a variant of the Model Win MDP introduced in Thomas & Brunskill (2016) to verify theoretical findings. |
| Dataset Splits | No | The paper describes the MDP environment and data generation process (offline dataset D consisting of K trajectories), but it does not specify explicit training, validation, and test dataset splits for the algorithms. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We evaluate the scenarios H = 5, 10, 15, 20 and β = 0.5, 1 in the experiment. ... The behavior policy we use to generate the offline data is taking a1 and a2 randomly with equal probability. |