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..
Continuous Doubly Constrained Batch Reinforcement Learning
Authors: Rasool Fakoor, Jonas W. Mueller, Kavosh Asadi, Pratik Chaudhari, Alexander J. Smola
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Over a comprehensive set of 32 continuous-action batch RL benchmarks, our approach compares favorably to state-of-the-art methods, regardless of how the offline data were collected.In this section, we evaluate our CDC algorithm against existing methods on 32 tasks from the D4RL benchmark [14]. We also investigate the utility of individual CDC regularizers through ablation analyses, and demonstrate the broader applicability of our extra-overestimation penalty to off-policy evaluation in addition to batch RL. |
| Researcher Affiliation | Collaboration | 1Amazon Web Services, 2University of Pennsylvania |
| Pseudocode | Yes | Algorithm 1 Continuous Doubly Constrained Batch RL |
| Open Source Code | No | The paper does not provide a concrete access link or explicit statement about the availability of the source code for the methodology described in this paper. It only mentions the code for a baseline method (CQL) from a reference: "The results for CQL are taken from the official author-provided codes [https://github.com/aviralkumar2907/CQL] of [29]." |
| Open Datasets | Yes | We compare CDC against existing batch RL methods... on 32 tasks from the D4RL benchmark [14]." Reference [14]: "J. Fu, A. Kumar, O. Nachum, G. Tucker, and S. Levine. D4rl: Datasets for deep data-driven reinforcement learning. ar Xiv:2004.07219, 2020." |
| Dataset Splits | No | The paper states: "Our training/evaluation setup exactly follows existing work [14, 17, 28, 29]," but it does not provide specific details on the training, validation, and test dataset splits (e.g., percentages, sample counts, or explicit citations to predefined splits) within its own text. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In CDC, we can simply utilize the same moderately conservative value of ν = 0.75 used by [17]... CDC is able to achieve strong performance with a small ensemble of M = 4 Q-networks (used throughout this work)... Throughout, we use η = 0 & λ = 0 to refer to this baseline framework (without our proposed penalties)... |