{"id":1471,"date":"2023-10-13T09:30:26","date_gmt":"2023-10-13T08:30:26","guid":{"rendered":"https:\/\/babel.isa.uma.es\/kipr\/?p=1471"},"modified":"2023-10-13T09:30:26","modified_gmt":"2023-10-13T08:30:26","slug":"modifications-of-q-learning-for-better-learning-of-robot-navigation","status":"publish","type":"post","link":"https:\/\/babel.isa.uma.es\/kipr\/?p=1471","title":{"rendered":"Modifications of Q-learning for better learning of robot navigation"},"content":{"rendered":"<h4>Ee Soong Low, Pauline Ong, Cheng Yee Low, Rosli Omar, <strong>Modified Q-learning with distance metric and virtual target on path planning of mobile robot, <\/strong>  Expert Systems with Applications, Volume 199, 2022, <a href=\"https:\/\/doi.org\/10.1016\/j.eswa.2022.117191\" target=\"_blank\">DOI: 10.1016\/j.eswa.2022.117191<\/a>.<\/h4>\n<blockquote><p>Path planning is an essential element in mobile robot navigation. One of the popular path planners is Q-learning \\u2013 a type of reinforcement learning that learns with little or no prior knowledge of the environment. Despite the successful implementation of Q-learning reported in numerous studies, its slow convergence associated with the curse of dimensionality may limit the performance in practice. To solve this problem, an Improved Q-learning (IQL) with three modifications is introduced in this study. First, a distance metric is added to Q-learning to guide the agent moves towards the target. Second, the Q function of Q-learning is modified to overcome dead-ends more effectively. Lastly, the virtual target concept is introduced in Q-learning to bypass dead-ends. Experimental results across twenty types of navigation maps show that the proposed strategies accelerate the learning speed of IQL in comparison with the Q-learning. Besides, performance comparison with seven well-known path planners indicates its efficiency in terms of the path smoothness, time taken, shortest distance and total distance used.<\/p><\/blockquote>\n","protected":false},"excerpt":{"rendered":"<p>Ee Soong Low, Pauline Ong, Cheng Yee Low, Rosli Omar, Modified Q-learning with distance metric and virtual target on path <span class=\"ellipsis\">&hellip;<\/span> <span class=\"more-link-wrap\"><a href=\"https:\/\/babel.isa.uma.es\/kipr\/?p=1471\" class=\"more-link\"><span>Read More &rarr;<\/span><\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14],"tags":[214,13,230],"class_list":["post-1471","post","type-post","status-publish","format-standard","hentry","category-applications-of-reinforcement-learning-to-robots","tag-path-planning","tag-q-learning","tag-robot-navigation"],"_links":{"self":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/1471"}],"collection":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1471"}],"version-history":[{"count":1,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/1471\/revisions"}],"predecessor-version":[{"id":1472,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/1471\/revisions\/1472"}],"wp:attachment":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1471"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1471"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1471"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}