{"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/reinforcement-learning-with-a-and-a-deep","title":"Reinforcement Learning with A* and a Deep Heuristic","arxiv_id":"1811.07745","date":"2018-11-19","proceeding":null,"authors":["Ariel Keselman","Sergey Ten","Adham Ghazali","Majed Jubeh"],"abstract":"A* is a popular path-finding algorithm, but it can only be applied to those\ndomains where a good heuristic function is known. Inspired by recent methods\ncombining Deep Neural Networks (DNNs) and trees, this study demonstrates how to\ntrain a heuristic represented by a DNN and combine it with A*. This new\nalgorithm which we call aleph-star can be used efficiently in domains where the\ninput to the heuristic could be processed by a neural network. We compare\naleph-star to N-Step Deep Q-Learning (DQN Mnih et al. 2013) in a driving\nsimulation with pixel-based input, and demonstrate significantly better\nperformance in this scenario.","url_abs":"http://arxiv.org/abs/1811.07745v1","url_pdf":"http://arxiv.org/pdf/1811.07745v1.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":"reinforcement-learning-with-a-and-a-deep","repo_url":"https://github.com/imagry/aleph_star","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"reinforcement-learning-with-a-and-a-deep","repo_url":"https://github.com/bjotho/Zelda1AI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"q-learning","task_name":"Q-Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[{"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}