Multi-View Reinforcement Learning
Authors: Minne Li, Lisheng Wu, Jun WANG, Haitham Bou Ammar
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate our method and demonstrate its effectiveness in a variety of environments. Specifically, we show reductions in sample complexities and computational time for acquiring policies that handle multi-view environments. [...] We evaluate our method on a variety of dynamical systems varying in dimensions of their state representation. We consider both high and low dimensional problems to demonstrate the effectiveness of our model-free and model-based solutions. |
| Researcher Affiliation | Academia | Minne Li University College London London, United Kingdom minne.li@cs.ucl.ac.uk Lisheng Wu University College London London, United Kingdom lisheng.wu.17@ucl.ac.uk Haitham Bou Ammar University College London London, United Kingdom haitham.bouammar71@googlemail.com Jun Wang University College London London, United Kingdom junwang@cs.ucl.ac.uk |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | We consider dynamical systems from the Atari suite, Roboschool [29], Py Bullet [8], and the Highway environments [20]. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. It mentions "training samples" but not how the data was split for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | Relevant parameter values and implementation details are listed in the Appendix C.2. |