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..
Diversification of Adaptive Policy for Effective Offline Reinforcement Learning
Authors: Yunseon Choi, Li Zhao, Chuheng Zhang, Lei Song, Jiang Bian, Kee-Eung Kim
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Mo DAP through experiments on the D4RL and Neo RL benchmarks, showcasing its performance superiority over state-of-the-art algorithms. |
| Researcher Affiliation | Collaboration | 1KAIST AI 2Microsoft Research Asia |
| Pseudocode | Yes | Algorithm 1 Mo DAP |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating the availability of its source code. |
| Open Datasets | Yes | We evaluate Mo DAP through experiments on the D4RL [Fu et al., 2020] and Neo RL [Qin et al., 2022] benchmarks |
| Dataset Splits | Yes | In the initial phase of pre-training the dynamics models, we divide the offline dataset into a training set and a validation set using an 8:2 ratio. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using SAC and GRU but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | In the initial phase of pre-training the dynamics models, we divide the offline dataset into a training set and a validation set using an 8:2 ratio. For each task, we construct a set of estimated models by training either 7 (for D4RL) or 15 (for Neo RL) models. After this training, we proceed to select the top 5 (for D4RL) or 10 (for Neo RL) models based on their predictive accuracy, which is evaluated on the validation set. |