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 | Conference PDF | Archive PDF | Plain Text | 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. |