bibtype	L4 - Prototype, methodology, f. module, software
ARLID	0646408
utime	20260304131628.0
mtime	20260223235959.9
title (primary) (eng)	Robust Sequential Decision-Making in Adversarial Environments: Codebase
publisher	pub_time 2025
keyword	Dynamic programming
keyword	adversarial machine learning (AdvML)
keyword	Multi-agent reinforcement learning (MARL)
keyword	robust reinforcement learning
keyword	Bayesian reinforcement learning
author (primary)	ARLID cav_un_auth*0491463 name1 Ružejnikov name2 Jurij institution UTIA-B full_dept (cz) Adaptivní systémy full_dept (eng) Department of Adaptive Systems department (cz) AS department (eng) AS country CZ share 100 garant K fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source	source_type online databáze source_size 90.89 kB url https://doi.org/10.6084/m9.figshare.30788984
cas_special	project project_id 101168272 agency EC country XE ARLID cav_un_auth*0492513 project project_id CA24136 agency EC country XE ARLID cav_un_auth*0504278 project project_id 2025A1013 agency Provozně Ekonomická Fakulta ČZU country CZ ARLID cav_un_auth*0504279 abstract (eng) This repository contains experimental datasets, results and configuration files supporting the research article „Robust Sequential Decision-Making in Adversarial Environments“. The associated study addresses reinforcement learning in non-stationary, adversarial environments where standard Markov Decision Process (MDP) assumptions are violated, introducing a model-based framework for Threatened Markov Decision Process (TMDP) that utilises Bayesian belief updates to compute robust policies. RIV IN FORD0 10000 FORD1 10100 FORD2 10102 reportyear 2026 num_of_auth 1 inst_support RVO:67985556 permalink https://hdl.handle.net/11104/0376313 cooperation ARLID cav_un_auth*0478849 name Provozně ekonomická fakulta, Česká zemědělská univerzita v Praze institution PEF CZU country CZ confidential S arlyear 2025 mrcbU10 2025