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..
Offline Model-based Adaptable Policy Learning
Authors: Xiong-Hui Chen, Yang Yu, Qingyang Li, Fan-Ming Luo, Zhiwei Qin, Wenjie Shang, Jieping Ye
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on Mu Jo Co controlling tasks with offline datasets. The results show that the proposed method can make robust decisions in out-of-support regions and achieve better performance than SOTA algorithms. |
| Researcher Affiliation | Collaboration | 1 National Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, China 2 AI Labs, Didi Chuxing 3 Polixir.ai |
| Pseudocode | Yes | Algorithm 1 Offline model-based adaptable policy learning |
| Open Source Code | Yes | We release our code at Github 2. 2https://github.com/xionghuichen/MAPLE |
| Open Datasets | Yes | We evaluate MAPLE on multiple offline Mu Jo Co tasks [16]. We test MAPLE in standard offline RL tasks with D4RL datasets [30]. |
| Dataset Splits | No | The paper uses an offline dataset but does not explicitly provide specific train/validation/test dataset splits (e.g., percentages, sample counts, or explicit standard benchmark splits). |
| Hardware Specification | Yes | For example, by using NVIDIA Tesla P40 and Xeon(R) E5-2630 to train the algorithms, the time overhead of MAPLE-200 is 10 times longer than MAPLE. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | All the details of MAPLE s training and evaluation are given in Appendix E and Appendix F. The horizon H is set to 10 in these tasks. The policy is trained for 1000 iterations in the policy learning stage. |