{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-deep-q-learning-agent-for-the-l-game-with","title":"A Deep Q-Learning Agent for the L-Game with Variable Batch Training","arxiv_id":"1802.06225","date":"2018-02-17","proceeding":null,"authors":["Petros Giannakopoulos","Yannis Cotronis"],"abstract":"We employ the Deep Q-Learning algorithm with Experience Replay to train an\nagent capable of achieving a high-level of play in the L-Game while\nself-learning from low-dimensional states. We also employ variable batch size\nfor training in order to mitigate the loss of the rare reward signal and\nsignificantly accelerate training. Despite the large action space due to the\nnumber of possible moves, the low-dimensional state space and the rarity of\nrewards, which only come at the end of a game, DQL is successful in training an\nagent capable of strong play without the use of any search methods or domain\nknowledge.","url_abs":"http://arxiv.org/abs/1802.06225v1","url_pdf":"http://arxiv.org/pdf/1802.06225v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-deep-q-learning-agent-for-the-l-game-with","repo_url":"https://github.com/petrosgk/L-Game-DQN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"q-learning","task_name":"Q-Learning"},{"task_slug":"self-learning","task_name":"Self-Learning"}],"methods":[{"method_slug":"experience-replay","method_name":"Experience Replay"},{"method_slug":"q-learning","method_name":"Q-Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}