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..
Compositional Conservatism: A Transductive Approach in Offline Reinforcement Learning
Authors: Yeda Song, Dongwook Lee, Gunhee Kim
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply COCOA to four state-of-the-art offline RL algorithms and evaluate them on the D4RL benchmark, where COCOA generally improves the performance of each algorithm. |
| Researcher Affiliation | Academia | Yeda Song , Dongwook Lee & Gunhee Kim Seoul National University EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Generation of Anchor-Seeking Trajectory |
| Open Source Code | Yes | The code is available at https://github.com/runamu/compositionalconservatism. |
| Open Datasets | Yes | We evaluate our method on the Gym-Mu Jo Co tasks in D4RL benchmark (Fu et al., 2020) |
| Dataset Splits | No | The paper mentions 'validation error' in the context of dynamics model selection but does not explicitly state the dataset splits (e.g., percentages or counts for train/validation/test) needed to reproduce the experiment. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU models, or cloud computing instance types used for running experiments. |
| Software Dependencies | No | The paper mentions reliance on 'Pytorch' via a reference but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | For both CQL and CQL+COCOA, we use α = 5.0 for all D4RL-Gym tasks... For IQL, we use the same hyperparameters described in the original paper... τ = 0.7 and β = 3.0... For MOPO, we search for the best penalty coefficient λ and rollout length hr... λ {0.1, 0.5, 1.0, 5.0, 10.0}, hr {1, 5, 7, 10}... |