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..
Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes
Authors: Tomáš Brázdil, Krishnendu Chatterjee, Petr Novotný, Jiří Vahala9794-9801
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implemented RAlph and evaluated it on two sets of benchmarks. The first one is a modified, perfectly observable version of Hallway (Pineau et al. 2003; Smith and Simmons 2004)... As a second benchmark, we consider a controllable random walk (RW). The results are summarized in Table 1. |
| Researcher Affiliation | Academia | 1Faculty of Informatics, Masaryk University, Brno, Czech Republic {xbrazdil, petr.novotny, xvahala1}@fi.muni.cz 2Institute of Science and Technology Austria, Klosterneuburg, Austria EMAIL |
| Pseudocode | Yes | Algorithm 1: Training and evaluation of RAlph. and Algorithm 2: The episode sampling of RAlph. |
| Open Source Code | Yes | Implementation can be found at https://github.com/snurkabill/ Master Thesis/releases/tag/AAAI_release |
| Open Datasets | Yes | We implemented RAlph and evaluated it on two sets of benchmarks. The first one is a modified, perfectly observable version of Hallway (Pineau et al. 2003; Smith and Simmons 2004) |
| Dataset Splits | No | The paper describes training and evaluation phases using episodes, but it does not specify explicit train/validation/test dataset splits with percentages or counts for the datasets used. |
| Hardware Specification | Yes | The test configuration was: CPU: Intel Xeon E5-2620 v2@2.1GHz (24 cores); 8GB heap size; Debian 8. |
| Software Dependencies | No | The test configuration was: CPU: Intel Xeon E5-2620 v2@2.1GHz (24 cores); 8GB heap size; Debian 8. |
| Experiment Setup | Yes | Input: MDP M (with a horizon H), risk bound Δ, no. of training episodes m, batch size n (from Algorithm 1) and C is a suitable exploration constant, a parameter fixed in advance of the computation. and Both algorithms were evaluated over 1000 episodes, with a timeout of 1 hour per evaluation. |