{"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/apes-a-python-toolbox-for-simulating","title":"APES: a Python toolbox for simulating reinforcement learning environments","arxiv_id":"1808.10692","date":"2018-08-31","proceeding":null,"authors":["Aqeel Labash","Ardi Tampuu","Tambet Matiisen","Jaan Aru","Raul Vicente"],"abstract":"Assisted by neural networks, reinforcement learning agents have been able to\nsolve increasingly complex tasks over the last years. The simulation\nenvironment in which the agents interact is an essential component in any\nreinforcement learning problem. The environment simulates the dynamics of the\nagents' world and hence provides feedback to their actions in terms of state\nobservations and external rewards. To ease the design and simulation of such\nenvironments this work introduces $\\texttt{APES}$, a highly customizable and\nopen source package in Python to create 2D grid-world environments for\nreinforcement learning problems. $\\texttt{APES}$ equips agents with algorithms\nto simulate any field of vision, it allows the creation and positioning of\nitems and rewards according to user-defined rules, and supports the interaction\nof multiple agents.","url_abs":"http://arxiv.org/abs/1808.10692v1","url_pdf":"http://arxiv.org/pdf/1808.10692v1.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":"apes-a-python-toolbox-for-simulating","repo_url":"https://github.com/aqeel13932/APES","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"apes-a-python-toolbox-for-simulating","repo_url":"https://github.com/BPrasad123/Data_Science_Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"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":{"atlas_url":"https://app.syntology.ai/?focus=1808.10692","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}