Stochastic World from a First-Person Perspective: Non-Stationary Grid Environment with Uncertainty

Stochastic World from a First-Person Perspective: Non-Stationary Grid Environment with Uncertainty | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 18 February 2026 V1 Latest version Share on Stochastic World from a First-Person Perspective: Non-Stationary Grid Environment with Uncertainty Author : Seyyed Ali Sadat Tavana 0009-0007-6584-2654 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177144033.38724848/v1 92 views 45 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Reinforcement learning (RL) has demonstrated remarkable success across diverse domains, yet the development of algorithms capable of operating effectively under non-stationary conditions remains a fundamental challenge. Existing environments often assume stationary dynamics or focus on domain-specific niches, limiting their utility for systematically investigating adaptation to changing environments. In this paper first-person view stochastic world (FPV-SW) is introduced, an RL environment designed specifically for benchmarking algorithms under nonstationarity and uncertainty. The environment is formalized as a non-stationary Markov Decision Problem on a 7x7 grid world, where the reward landscape evolves through systematic combinations of eight distinct grid modes and three fog conditions, creating 24 unique environmental configurations. Stochastic transition dynamics introduce action uncertainty, while fog conditions modulate reward observability, enabling fine-grained control over environmental variability. The environment provides both discrete state representations and first-person views for partial observability studies. Statistical analysis of the reward structure represents significant variation across configurations, providing a rich benchmark for evaluating algorithm performance, adaptation rates, and robustness. Supplementary Material File (v_4.pdf) Download 677.18 KB Information & Authors Information Version history V1 Version 1 18 February 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords benchmark environment grid world non-stationary mdp partial observability reinforcement learning uncertainty Authors Affiliations Seyyed Ali Sadat Tavana 0009-0007-6584-2654 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 92 views 45 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Seyyed Ali Sadat Tavana. Stochastic World from a First-Person Perspective: Non-Stationary Grid Environment with Uncertainty. Authorea . 18 February 2026. DOI: https://doi.org/10.22541/au.177144033.38724848/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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