Memory-Augmented Monte Carlo Tree Search
Authors: Chenjun Xiao, Jincheng Mei, Martin Müller
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate MMCTS in the game of Go. Experimental results show that MMCTS outperforms the original MCTS with the same number of simulations. |
| Researcher Affiliation | Academia | Chenjun Xiao, Jincheng Mei, Martin Müller Computing Science, University of Alberta Edmonton, Canada {chenjun,jmei2,mmueller}@ualberta.ca |
| 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 | Our implementation applies a deep convolutional neural network (DCNN) from (Clark and Storkey 2015), which is trained for move prediction by professional game records. |
| 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. |
| 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 | The feature hashing dimension is set to 4096... The hash code in our Sim Hash implementation has 16 bits. We use 8 hash tables... set to 1 in our implementation. The discount parameter η in equation (9)... is set to 2... We set CNN STRENGTH to 200 in our experiment... we restrict DCNN calls to the first 100 nodes visited during the search... The parameter M is chosen from {20, 50, 100}, and τ from {0.05, 0.1, 1}. The size of M is set to 10000. We vary the number of simulations per move from {1000, 5000, 10000}. |