Robust Sequential Decision-Making in Adversarial Environments: Datasets and results

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. The provided repository contains the empirical data collected from 1,000 independent trials, each comprising 40,000 games, documenting the cumulative rewards per episode and terminal outcome ratios (win/loss/draw). These resources are provided to facilitate reproducibility of the primary findings.<br><br>The source code used to generate this data is available at link.