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..
Maximum Entropy Monte-Carlo Planning
Authors: Chenjun Xiao, Ruitong Huang, Jincheng Mei, Dale Schuurmans, Martin Müller
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results also demonstrate that MENTS is more sample efficient than UCT in both synthetic problems and Atari 2600 games. |
| Researcher Affiliation | Collaboration | Chenjun Xiao1 Jincheng Mei1 Ruitong Huang2 Dale Schuurmans1 Martin M uller1 1University of Alberta 2Borealis AI |
| Pseudocode | No | Section 4.1 “Algorithmic Design” describes the steps of MENTS, but it does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We then test MENTS on five Atari games: Beam Rider, Breakout, Q*bert, Seaquest and Space Invaders. |
| Dataset Splits | No | The paper mentions training a DQN model, but it does not provide specific details about dataset splits (e.g., train/validation/test percentages or counts) for the experiments conducted with MENTS or UCT. It refers to an Appendix for setup details, which is not provided. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as exact GPU/CPU models, processor types, or memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper mentions using a “vanilla DQN” but does not specify any software dependencies (e.g., libraries or solvers) with version numbers that would be needed to replicate the experiment setup. |
| Experiment Setup | Yes | The temperature is set to 0.1. At each time step we use 500 simulations to generate a move. The UCT algorithm adopts the following tree-policy introduced in Alpha Go [13], PUCT(s, a) = Q(s, a) + c P(s, a) / (b N(s, b) + 1 + N(s, a)). |