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..
Weighted Policy Constraints for Offline Reinforcement Learning
Authors: Zhiyong Peng, Changlin Han, Yadong Liu, Zongtan Zhou
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our algorithm outperforms existing state-of-the-art offline RL algorithms on the D4RL offline gym datasets. |
| Researcher Affiliation | Academia | College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China |
| Pseudocode | Yes | Algorithm 1: Weighted Policy Constraints |
| Open Source Code | Yes | The source code is available at https://github.com/qsa-fox/wPC. |
| Open Datasets | Yes | D4RL (Fu et al. 2020) is one of the main evaluation environments for offline RL, which consists of a wide of tasks and diverse datasets. |
| Dataset Splits | Yes | We run 1 million steps for training, evaluate policy every 5 thousand steps, and report the average normalized returns of 10 evaluation episodes as the score. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies (e.g., Python, PyTorch, TensorFlow versions) used in the experiments. |
| Experiment Setup | Yes | The only hyper-parameter attached to standard online RL is the α, which regulates the constraint strength. We set α to 0.1 for medium-expert datasets and 2.5 for others. ... Other hyper-parameters for TD3 components are presented in Table 3, and the neural network architectures are presented in Table 4. |