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.
Rethinking Value Function Learning for Generalization in Reinforcement Learning
Authors: Seungyong Moon, JunYeong Lee, Hyun Oh Song
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 17,911 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,200 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our proposed algorithms significantly improve observational generalization performance and sample efficiency on the Procgen Benchmark. |
| Researcher Affiliation | Collaboration | 1Seoul National University, 2Neural Processing Research Center, 3Deep Metrics |
| Pseudocode | Yes | Algorithm 1 Dynamics-aware Delayed-Critic Policy Gradient (DDCPG) |
| Open Source Code | Yes | The code can be found at https://github.com/snu-mllab/DCPG. |
| Open Datasets | Yes | In this paper, we utilize the Procgen benchmark as a testbed for observational generalization [10]. |
| Dataset Splits | No | The paper describes training and testing on the Procgen benchmark but does not explicitly mention a validation dataset or split details for it. |
| Hardware Specification | No | The paper states: 'We describe the computational resource in the supplementary material.', implying these details are not in the main body of the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers in the main text. |
| Experiment Setup | No | For the implementation details and hyperparameters, please refer to Appendix D. |