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..
Policy-Based Primal-Dual Methods for Convex Constrained Markov Decision Processes
Authors: Donghao Ying, Mengzi Amy Guo, Yuhao Ding, Javad Lavaei, Zuo-Jun Shen
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we validate algorithm (11) in a feasibility constrained MDP problem (cf. Example 2.2). The experiment is performed on the single agent version of Open AI Particle environment (Lowe et al. 2017) as illustrated in Figure 1a. |
| Researcher Affiliation | Academia | UC Berkeley, Department of Industrial Engineering and Operations Research EMAIL |
| Pseudocode | Yes | In Appendix A.1, we provide a sample-based pseudocode for algorithm (11). |
| Open Source Code | No | No explicit statement about providing open-source code or a link to a repository was found. |
| Open Datasets | Yes | The experiment is performed on the single agent version of Open AI Particle environment (Lowe et al. 2017) as illustrated in Figure 1a. |
| Dataset Splits | No | The paper describes the environment and task, but does not provide specific details on training, validation, or test dataset splits or percentages. |
| Hardware Specification | No | No specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running experiments were mentioned. |
| Software Dependencies | No | The paper mentions 'a two-layer fully-connected neural network' and 'REINFORCE-based method', but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | The policy is parameterized by a two-layer fully-connected neural network with 64 neurons in each layer and Re LU activations. We estimate the policy gradient ฮธL(ฮธ,ยต) through the REINFORCE-based method (Zhang et al. 2021) with n = 10 and K = 25 (see Algorithm 1 in Appendix A.1). The feasibility constraint has a threshold of d0 = 0.2. |