Monte Carlo Tree Search in Continuous Action Spaces with Execution Uncertainty
Authors: Timothy Yee, Viliam Lisy, Michael Bowling
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In a high fidelity simulator of the Olympic sport of curling, we show that this approach significantly outperforms existing MCTS methods. We evaluate KR-UCT in a high fidelity simulation of the Olympic sport of curling. |
| Researcher Affiliation | Academia | Timothy Yee, Viliam Lis y, Michael Bowling Department of Computing Science University of Alberta Edmonton, AB, Canada T6G 2E8 {tayee, lisy, bowling}@ualberta.ca |
| Pseudocode | Yes | Algorithm 1 Kernel Regression UCT |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes using a 'high fidelity simulator of the Olympic sport of curling' and models based on 'Olympic-level players' and 'curling percentage statistics of men from the 2010 and 2014 Olympic games' to fit parameters. However, it does not provide a specific link, DOI, or formal citation to a publicly accessible dataset used for training or evaluation. |
| Dataset Splits | No | The paper describes experimental evaluation over simulated games, mentioning '1600 samples' and '16000 one-end games', but it does not specify explicit training, validation, and test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The acknowledgements section mentions 'The computational resources were made possible by Compute Canada and Calcul Qu ebec.' This is a general statement about resources and does not provide specific hardware details (e.g., specific GPU/CPU models, memory specifications) used for the experiments. |
| Software Dependencies | No | The paper states, 'The curling simulator used in this paper is implemented using the Chipmunk 2D rigid body physics library'. However, it does not provide a version number for this library or any other software dependencies with specific version information, which is necessary for reproducibility. |
| Experiment Setup | Yes | All algorithms used 1600 samples and evaluated the final shot selection with a lower confidence bound estimate (CLCB = 0.001). For each algorithm, we ran a round robin tournament to identify a good UCB constant from the set {0.01, 0.1, 1.0, 10, 100}. For all algorithms, CUCB = 1.0 was the best constant. For the weighting in RAVE, we did a similar round robin tournament to select the β parameter from the set {0.01, 0.1, 1.0, 10.0, 100.0}, and found β = 1.0 to be the best for RAVE and RAVE+PW. For KR-UCT, we defined = 0.02 and k = 10. |