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..
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes
Authors: Mengdi Xu, Wenhao Ding, Jiacheng Zhu, ZUXIN LIU, Baiming Chen, Ding Zhao
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, our approach outperforms alternative methods in non-stationary tasks, including classic control with changing dynamics and decision making in different driving scenarios. |
| Researcher Affiliation | Academia | Carnegie Mellon University EMAIL |
| Pseudocode | Yes | Algorithm 1: Bayesian Inference for Continual Online Model-based Reinforcement Learning |
| Open Source Code | Yes | Codes available at: https://github.com/mxu34/mbrl-gpmm. |
| Open Datasets | No | The paper mentions using environments like 'Cartpole-Swing Up, Half Cheetah and Highway Intersection', but does not provide concrete access information (link, DOI, citation) to specific datasets used for training, validation, or testing. |
| Dataset Splits | No | The paper does not provide specific percentages or counts for training, validation, or test dataset splits. It describes an online learning setting with streaming data. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size) or detailed training configurations in the main text. |