Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Bhat, Soujanya M; Ramasamy, Gobi; Syam Mohan, E. Title A name given to the resource Solving Wordle Using Actor-Critic Reinforcement Learning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 9th International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/CSITSS67709.2025.11294192" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CSITSS67709.2025.11294192</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105031416529?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105031416529?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Bhat S.M., Christ University, Department of Computer Science, Bangalore, India; Ramasamy G., Christ University, Department of Computer Science, Bangalore, India; Syam Mohan E., Christ University, Department of Computer Science, Bangalore, India Description An account of the resource The popular word-game Wordle poses a difficult sequential decision-making problem with enormous discrete action spaces and partial feedback. In order to solve Wordle as efficiently as possible, this work explores the use of actor-critic reinforcement learning techniques. We develop two actor-critic variations, Vanilla Actor-Critic (AC) and Advantage Actor-Critic (A2C), and formulate Wordle as a Markov Decision Process. Through curriculum training on increasingly larger vocabulary, our method filters out invalid actions and guides learning by combining neural networks and symbolic reasoning. While the AC agent has 42.35 % success with an average guess of 4.85, the A2C agent has a 46.05 % success rate averaging 5.31 guesses per successful game. We show that, especially in the worst-case situations, batch-based A2C performs more robustly than stepbased AC. By effectively scaling from tiny vocabularies (50 words) to the entire Wordle lexicon (14,855 words), our neuro-symbolic technique demonstrates the efficacy of curriculum learning for challenging word games. 2025 IEEE. Subject The topic of the resource actor-critic methods; curriculum learning; neuro-symbolic AI; reinforcement learning; word games; wordle Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158894-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online