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 | Conference PDF | Archive PDF | Plain Text | 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 {mengdixu, wenhaod, jzhu4, zuxinl, baimingc, dingzhao}@andrew.cmu.edu |
| 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. |