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..
Generalized Mean Estimation in Monte-Carlo Tree Search
Authors: Tuan Dam, Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We theoretically analyze our method providing guarantees of convergence to the optimum. Finally, we empirically demonstrate the effectiveness of our method in well-known MDP and POMDP benchmarks, showing significant improvement in performance and convergence speed w.r.t. state of the art algorithms. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Technische Universit at Darmstadt, Germany 2Robot Learning Group, Max Planck Institute for Intelligent Systems,T ubingen, Germany 3Computing Sciences, Tampere University, Finland |
| Pseudocode | Yes | Algorithm 1 Power-UCT |
| Open Source Code | No | No explicit statement about providing open-source code for their methodology or a link to a repository. |
| Open Datasets | Yes | For MDPs, we consider the well-known Frozen Lake problem as implemented in Open AI Gym [Brockman et al., 2016]. |
| Dataset Splits | No | The paper does not specify explicit training, validation, and test dataset splits by percentage or sample count. It mentions 'evaluation runs' but not data partitioning for model training and selection. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU/GPU models, memory, or computational resources used for running experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | For MENTS we find the best combination of the two hyperparameters by grid search. In MDP tasks, we find the UCT exploration constant using grid search. For Power-UCT, we find the p-value by increasing it until performance starts to decrease. |