{"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-threshold-based-scheme-for-reinforcement","title":"A Threshold-based Scheme for Reinforcement Learning in Neural Networks","arxiv_id":"1609.03348","date":"2016-09-12","proceeding":null,"authors":["Thomas H. Ward"],"abstract":"A generic and scalable Reinforcement Learning scheme for Artificial Neural\nNetworks is presented, providing a general purpose learning machine. By\nreference to a node threshold three features are described 1) A mechanism for\nPrimary Reinforcement, capable of solving linearly inseparable problems 2) The\nlearning scheme is extended to include a mechanism for Conditioned\nReinforcement, capable of forming long term strategy 3) The learning scheme is\nmodified to use a threshold-based deep learning algorithm, providing a robust\nand biologically inspired alternative to backpropagation. The model may be used\nfor supervised as well as unsupervised training regimes.","url_abs":"http://arxiv.org/abs/1609.03348v4","url_pdf":"http://arxiv.org/pdf/1609.03348v4.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-threshold-based-scheme-for-reinforcement","repo_url":"https://github.com/thward/neural_agent","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"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":[],"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}