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..
Conservative Dual Policy Optimization for Efficient Model-Based Reinforcement Learning
Authors: Shenao Zhang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results also validate the exploration efficiency of CDPO. (Abstract) and 6 Empirical Evaluation (Section 6) |
| Researcher Affiliation | Academia | Shenao Zhang Georgia Institute of Technology Atlanta, GA 30332 EMAIL |
| Pseudocode | Yes | Algorithm 1 Practical CDPO Algorithm |
| Open Source Code | Yes | Our code can be found in the supplemental material. |
| Open Datasets | No | The paper uses standard RL environments (Mu Jo Co tasks, N-Chain MDPs) for experimentation, but does not provide concrete access information or citations for specific datasets in the way a supervised learning paper would. |
| Dataset Splits | No | The paper does not explicitly mention training, validation, or test dataset splits, nor does it provide specific percentages or sample counts for these splits in the provided text. |
| Hardware Specification | No | The provided text does not specify any hardware details such as GPU models, CPU types, or cloud computing instances used for running the experiments. |
| Software Dependencies | No | The paper mentions using Dyna and MPC solvers, neural network ensembles, and specific optimization methods like Adam (via citation), but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Implementation details and hyperparameters are provided in Appendix F.1. |