POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis
Authors: Weichao Mao, Kaiqing Zhang, Qiaomin Xie, Tamer Basar
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further provide experimental results that corroborate our theoretical findings. |
| Researcher Affiliation | Academia | Weichao Mao ECE and CSL University of Illinois at Urbana-Champaign weichao2@illinois.edu Kaiqing Zhang ECE and CSL University of Illinois at Urbana-Champaign kzhang66@illinois.edu Qiaomin Xie ORIE Cornell University qiaomin.xie@cornell.edu Tamer Ba sar ECE and CSL University of Illinois at Urbana-Champaign basar1@illinois.edu |
| Pseudocode | Yes | Our algorithm for continuous space MCTS, Polynomial Hierarchical Optimistic Optimization applied to Trees (POLY-HOOT), is presented in Algorithm 1. |
| Open Source Code | No | The paper does not provide a link to open-source code for the described methodology or state that it is available. |
| Open Datasets | Yes | We have chosen three benchmark tasks from Open AI Gym (Open AI, 2016) |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, or test splits. For the control tasks, performance is averaged over runs rather than using predefined dataset splits. |
| Hardware Specification | Yes | All tests are averaged over 10 (new) runs on a laptop with an Intel Core i5-9300H CPU. |
| Software Dependencies | No | The paper mentions 'Open AI Gym (Open AI, 2016)' but does not provide specific version numbers for this or any other software dependencies. |
| Experiment Setup | No | The paper states that 'The detailed experiment settings as well as additional experiment results can be found in Appendix E.' but these details are not present in the provided text. |