Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
ASPiRe: Adaptive Skill Priors for Reinforcement Learning
Authors: Mengda Xu, Manuela Veloso, Shuran Song
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 17,571 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,247 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that ASPi Re can significantly accelerate the learning of new downstream tasks in the presence of multiple priors and show improvement on competitive baselines. We evaluate our method in three modified environments from D4RL [48] and one modified environment from robosuite environment [49]. |
| Researcher Affiliation | Collaboration | Mengda Xu 1, 2, Manuela Veloso 2,3, Shuran Song 1 1 Department of Computer Science, Columbia University 2 J.P. Morgan AI Research 3 School of Computer Science, Carnegie Mellon University (emeritus) |
| Pseudocode | Yes | Algorithm 1 ASPi Re Algorithm |
| Open Source Code | Yes | The code is in the supplement material. We provide the codes to regenerate the datasets and downstream task learning and all instructions are inside readme.md |
| Open Datasets | Yes | We evaluate our method in three modified environments from D4RL [48] and one modified environment from robosuite environment [49]. We use the D4RL benchmark and cite in the section 4. |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See appendix A.4 |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See appendix A.4 |
| Software Dependencies | No | No specific software version numbers (e.g., Python 3.x, PyTorch 1.x) are explicitly mentioned in the paper's main text or checklist for reproducibility. |
| Experiment Setup | Yes | See appendix A.4 for details on environment, data collection process and training. Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See appendix A.4 |