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 for Constrained POMDPs
Authors: Jongmin Lee, Geon-hyeong Kim, Pascal Poupart, Kee-Eung Kim
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, we demonstrate that CC-POMCP converges to the optimal stochastic action selection in CPOMDP and pushes the state-of-the-art by being able to scale to very large problems. |
| Researcher Affiliation | Collaboration | Jongmin Lee1, Geon-Hyeong Kim1, Pascal Poupart2, Kee-Eung Kim1,3 1 School of Computing, KAIST, Republic of Korea 2 University of Waterloo, Waterloo AI Institute and Vector Institute 3 PROWLER.io |
| Pseudocode | Yes | Algorithm 1 Cost-Constrained POMCP (CC-POMCP) |
| Open Source Code | No | The paper does not provide any explicit statement about releasing its source code or a link to a code repository. |
| Open Datasets | Yes | We first tested CC-POMCP on the synthetic toy domain introduced in [11] to demonstrate convergence to stochastic optimal actions, where the cost constraint ˆc is 0.95. We also conducted experiments on a multi-objective version of PONG, an arcade game running on the Arcade Learning Environment (ALE) [3], depicted in Figure 2a. |
| Dataset Splits | No | The paper mentions using different domains (Toy, Rocksample, PONG) for experiments but does not provide specific training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with versions like Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | All the parameters for running CC-POMCP are provided in Appendix H. |