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..
Off-Policy Deep Reinforcement Learning without Exploration
Authors: Scott Fujimoto, David Meger, Doina Precup
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present the ๏ฌrst continuous control deep reinforcement learning algorithm which can learn effectively from arbitrary, ๏ฌxed batch data, and empirically demonstrate the quality of its behavior in several tasks. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Mc Gill University, Montreal, Canada 2Mila Qu ebec AI Institute. |
| Pseudocode | Yes | Algorithm 1 BCQ |
| Open Source Code | Yes | To ensure reproducibility, we provide precise experimental and implementation details, and our code is made available (https://github.com/sfujim/BCQ). |
| Open Datasets | Yes | Our practical experiments examine three different batch settings in Open AI gym s Hopper-v1 environment (Todorov et al., 2012; Brockman et al., 2016) |
| Dataset Splits | No | The paper describes data collection and usage in different batch settings but does not explicitly provide train/validation/test dataset splits or cross-validation details. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Open AI gym' and 'Mu Jo Co environments' but does not provide specific version numbers for these or any other ancillary software dependencies. |
| Experiment Setup | Yes | Exact implementation and experimental details are provided in the Supplementary Material. |