{"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/creating-hierarchical-dispositions-of-needs","title":"Creating Hierarchical Dispositions of Needs in an Agent","arxiv_id":"2412.00044","date":"2024-11-23","proceeding":null,"authors":["Tofara Moyo"],"abstract":"We present a novel method for learning hierarchical abstractions that prioritize competing objectives, leading to improved global expected rewards. Our approach employs a secondary rewarding agent with multiple scalar outputs, each associated with a distinct level of abstraction. The traditional agent then learns to maximize these outputs in a hierarchical manner, conditioning each level on the maximization of the preceding level. We derive an equation that orders these scalar values and the global reward by priority, inducing a hierarchy of needs that informs goal formation. Experimental results on the Pendulum v1 environment demonstrate superior performance compared to a baseline implementation.We achieved state of the art results.","url_abs":"https://arxiv.org/abs/2412.00044v1","url_pdf":"https://arxiv.org/pdf/2412.00044v1.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":"creating-hierarchical-dispositions-of-needs","repo_url":"https://github.com/TofaraMoyo/Heirachical-Reward-Functions","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"openai-gym","task_name":"OpenAI Gym"},{"task_slug":null,"task_name":"Pendulum-v1"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/openai-gym-on-pendulum-v1","task":"OpenAI Gym","dataset":"Pendulum-v1","model":"TLA with Hierarchical Reward Functions","rank_in_archive_order":1,"of":2,"metrics":{"Action Repetition":".8073","Average Decisions":"38.6","Mean Reward":"-125.02"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}