{"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/towards-a-common-implementation-of","title":"Towards a Common Implementation of Reinforcement Learning for Multiple Robotic Tasks","arxiv_id":"1702.06329","date":"2017-02-21","proceeding":null,"authors":["Angel Martínez-Tenor","Juan Antonio Fernández-Madrigal","Ana Cruz-Martín","Javier González-Jiménez"],"abstract":"Mobile robots are increasingly being employed for performing complex tasks in\ndynamic environments. Reinforcement learning (RL) methods are recognized to be\npromising for specifying such tasks in a relatively simple manner. However, the\nstrong dependency between the learning method and the task to learn is a\nwell-known problem that restricts practical implementations of RL in robotics,\noften requiring major modifications of parameters and adding other techniques\nfor each particular task. In this paper we present a practical core\nimplementation of RL which enables the learning process for multiple robotic\ntasks with minimal per-task tuning or none. Based on value iteration methods,\nthis implementation includes a novel approach for action selection, called\nQ-biased softmax regression (QBIASSR), which avoids poor performance of the\nlearning process when the robot reaches new unexplored states. Our approach\ntakes advantage of the structure of the state space by attending the physical\nvariables involved (e.g., distances to obstacles, X,Y,{\\theta} pose, etc.),\nthus experienced sets of states may favor the decision-making process of\nunexplored or rarely-explored states. This improvement has a relevant role in\nreducing the tuning of the algorithm for particular tasks. Experiments with\nreal and simulated robots, performed with the software framework also\nintroduced here, show that our implementation is effectively able to learn\ndifferent robotic tasks without tuning the learning method. Results also\nsuggest that the combination of true online SARSA({\\lambda}) with QBIASSR can\noutperform the existing RL core algorithms in low-dimensional robotic tasks.","url_abs":"http://arxiv.org/abs/1702.06329v1","url_pdf":"http://arxiv.org/pdf/1702.06329v1.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":"towards-a-common-implementation-of","repo_url":"https://github.com/angelmtenor/RL-ROBOT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"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":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}