The Option Keyboard: Combining Skills in Reinforcement Learning
Authors: Andre Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan hunt, Shibl Mourad, David Silver, Doina Precup
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now present our experimental results illustrating the benefits of OK in practice. Additional details, along with further results and analysis, can be found in Appendix C. |
| Researcher Affiliation | Industry | André Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan Hunt, Shibl Mourad, David Silver, Doina Precup {andrebarreto,borsa,shaobohou,gcomanici,eser}@google.com {hamelphi,kenjitoyama,jjhunt,shibl,davidsilver,doinap}@google.com |
| Pseudocode | Yes | Algorithm 1 Option Keyboard (OK) |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a direct link to a code repository for the methodology described. |
| Open Datasets | No | The paper describes custom environments ('Foraging world', 'Moving-target arena' in MuJoCo) for its experiments and does not specify the use or provision of any publicly available datasets. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like the 'Mu Jo Co physics engine' but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | No | The paper describes the general setup of the environments and how cumulants were defined, but it does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) for the learning algorithms used. |