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..
Monte-Carlo Tree Search in Continuous Action Spaces with Value Gradients
Authors: Jongmin Lee, Wonseok Jeon, Geon-Hyeong Kim, Kee-Eung Kim4561-4568
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, we demonstrate that our approach outperforms existing MCTS methods and other strong baseline algorithms for continuous action spaces. |
| Researcher Affiliation | Academia | 1School of Computing, KAIST, Daejeon, Republic of Korea 2Graduate School of AI, KAIST, Daejeon, Republic of Korea 3Mila, Quebec AI Institute, Montreal, Canada 4Mc Gill University, Montreal, Canada |
| Pseudocode | Yes | Algorithm 1 Value-Gradient UCT (VG-UCT) |
| 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 | Realistic experiments were conducted on four stochastic variants of continuous control tasks: Pendulum (continuous action version), Acrobot (continuous action version), Reacher and Pusher from the Open AI Gym environment (Brockman et al. 2016; Todorov, Erez, and Tassa 2012) |
| 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 mentions using the "Open AI Gym environment" but does not specify version numbers for Gym or other ancillary software dependencies, which are necessary for replication. |
| Experiment Setup | No | Detailed experimental settings are provided in Appendix D. |