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..
OFFER: Off-Environment Reinforcement Learning
Authors: Kamil Ciosek, Shimon Whiteson
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically compare OFFER to a policy gradient baseline (primary optimisation only) on variants of the mountain car task, as well as a simulated robot arm. |
| Researcher Affiliation | Academia | Kamil Ciosek, Shimon Whiteson Department of Computer Science, University of Oxford, United Kingdom EMAIL |
| Pseudocode | Yes | Algorithm 1 CRITIC-REINFORCE(τ) ... Algorithm 5 SECONDARY-OPTIMISATION(τ, ψ) |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | No | The paper mentions the 'mountain car benchmark task' and a 'simulated robotic-arm control task' proposed by Paul et al. (2016) but does not provide concrete access information (link, DOI, repository, or clear citation to a publicly available dataset) for the specific datasets or environments used in their modified experiments. |
| Dataset Splits | No | The paper does not explicitly state specific training, validation, or test dataset splits or percentages. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, memory, or cluster specifications used for running its experiments. |
| Software Dependencies | No | The paper mentions algorithms like ADAM but does not provide specific software names with version numbers for reproducibility (e.g., 'Python 3.x', 'PyTorch 1.x'). |
| Experiment Setup | No | The paper states 'complete details are in the supplementary material' for the experimental setup, and the main text only provides general descriptions like 'θ consists of the parameters of a standard tile-coding function approximator' and 'the agent moves each joint by at most 30% of the allowed movement range', without specific hyperparameter values or comprehensive system-level training settings. |