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 [1].
A Closer Look at Offline RL Agents
Authors: Yuwei Fu, Di Wu, Benoit Boulet
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this work, we first introduce a set of experiments to evaluate offline RL agents, focusing on three fundamental aspects: representations, value functions and policies. |
| Researcher Affiliation | Academia | Yuwei Fu, Di Wu, Benoit Boulet McGill University EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at: https://github.com/fuyw/RIQL. |
| Open Datasets | Yes | on the standard D4RL dataset [14]. |
| Dataset Splits | Yes | For each probing target, we use a 5-fold cross-validation on Dprobe to train a linear regression model with Mean Squared Error (MSE) loss. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments in the provided text. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper mentions training details but does not provide specific hyperparameter values or comprehensive system-level training settings in the provided text. |