Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multiple Policy Value Monte Carlo Tree Search
Authors: Li-Cheng Lan, Wei Li, Ting-Han Wei, I-Chen Wu
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show through experiments on the game No Go that a combined f S and f L MPV-MCTS outperforms single PV-NN with policy value MCTS, called PV-MCTS. |
| Researcher Affiliation | Academia | 1Department of Computer Science, National Chiao Tung University, Taiwan 2Pervasive Arti๏ฌcial Intelligence Research (PAIR) Labs, Taiwan EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: MPV-MCTS Algorithm |
| Open Source Code | No | The paper mentions 'Ha Ha No Go' as an open-source program they used as a baseline, with a link to its GitHub repository. However, it does not provide a link or explicit statement about the open-sourcing of the 'MPV-MCTS' implementation itself. |
| Open Datasets | No | The paper states, 'We trained both f64,5 and f128,10 from a dataset of 200,000 games (about 107 positions) generated by Ha Ha No Go with 50,000 simulations for each move via self-play.' While Ha Ha No Go is an open-source program, there is no explicit link, DOI, or formal citation provided for the generated dataset itself. |
| Dataset Splits | No | The paper describes training processes and self-play game generation but does not provide specific details on train/validation/test dataset splits, such as percentages, sample counts, or citations to predefined splits. |
| Hardware Specification | Yes | In this paper, all experiments are performed on eight Intel Xeon(R) Gold 6154 CPUs and 64 Nvidia Tesla V100 GPUs. |
| Software Dependencies | No | The paper mentions various algorithms and models such as MCTS, DNNs, PV-NNs, and Alpha Go Zero, but it does not list specific software dependencies with their version numbers (e.g., programming languages, libraries, or frameworks like PyTorch, TensorFlow, or CUDA versions). |
| Experiment Setup | Yes | We trained both f64,5 and f128,10 using the following settings: simulation count: 800, PUCT constant: 1.5, learning rate: 0.05, batch size: 1024. |