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..
Counterfactual Online Learning for Open-Loop Monte-Carlo Planning
Authors: Thomy Phan, Shao-Hung Chan, Sven Koenig
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate CORAL in four POMDP benchmark scenarios and compare it with closed-loop and open-loop alternatives. |
| Researcher Affiliation | Academia | 1University of Southern California 2University of California, Irvine EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Thompson Sampling; Algorithm 2: CORAL Planning |
| Open Source Code | Yes | Code github.com/thomyphan/counterfactual-planning |
| Open Datasets | Yes | Tag is a gridworld challenge where the agent has to find and tag a target that intentionally moves away. ... Rock Sample[K,L] consists of a K K grid with L rocks (Smith and Simmons 2004). ... Poc Man is a partially observable version of Pac Man (Silver and Veness 2010). |
| Dataset Splits | No | The paper evaluates its method using simulation environments and does not mention static dataset splits (training/test/validation) in the traditional sense. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions several algorithms and existing implementations (e.g., 'POMCP and POMCPOW implementations are based on the code from (Silver and Veness 2010)', 'open-loop MCTS implementation from (Phan et al. 2019a)') but does not specify software names with version numbers. |
| Experiment Setup | Yes | For each environment, we use γ = 0.95 and the respective preferred action sets Apref (Silver and Veness 2010). ... Unless stated otherwise, we always set η = 50% for CORAL. ... We ran experiments for different simulation budgets nb. For each budget nb, we tested 100 random instances to report the average performance and the 95% confidence interval. |