{"id":4542,"date":"2020-06-24T09:25:27","date_gmt":"2020-06-24T01:25:27","guid":{"rendered":"http:\/\/pairlabs.ai.pro6.designworks.tw\/?post_type=portfolio&#038;p=4542"},"modified":"2020-07-22T15:28:38","modified_gmt":"2020-07-22T07:28:38","slug":"studies-of-applications-with-deep-reinforcement-learning-technologies-p-en","status":"publish","type":"portfolio","link":"https:\/\/pairlabs.ai\/en\/portfolio-item\/studies-of-applications-with-deep-reinforcement-learning-technologies-p-en\/","title":{"rendered":"Studies of Applications with Deep Reinforcement Learning Technologies"},"content":{"rendered":"<\/div><\/div><\/div><!-- close content main div --><\/div><\/div><div id='av_section_1' class='avia-section main_color avia-section-large avia-no-border-styling avia-full-stretch av-section-color-overlay-active avia-bg-style-fixed    av-small-hide av-mini-hide container_wrap sidebar_right' style='background-repeat: no-repeat; background-image: url(https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/05\/wall005.jpg);background-attachment: fixed; background-position: bottom right;  '  data-section-bg-repeat='stretch' style='background-repeat: no-repeat; background-image: url(https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/05\/wall005.jpg);background-attachment: fixed; background-position: bottom right;  ' ><div class='av-section-color-overlay-wrap'><div class='av-section-color-overlay' style='opacity: 0.6; background-color: #ffffff; '><\/div><div class='container' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-4542'><div class='entry-content-wrapper clearfix'>\n<div class='flex_column_table av-equal-height-column-flextable -flextable' style='margin-top:0px; margin-bottom:-20px; '><div class=\"flex_column av_one_fourth  flex_column_table_cell av-equal-height-column av-align-top av-zero-column-padding first   \" style='border-radius:0px; '><\/div><\/div><!--close column table wrapper. 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Autoclose: 1 -->\n<\/div><\/div><\/div><!-- close content main div --><\/div><\/div><\/div><div id='after_section_1' class='main_color av_default_container_wrap container_wrap sidebar_right' style=' '   style=' ' ><div class='container' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-4542'><div class='entry-content-wrapper clearfix'>\n<\/div><\/div><\/div><!-- close content main div --><\/div><\/div><div id='av_section_2' class='avia-section main_color avia-section-large avia-no-border-styling avia-full-stretch av-section-color-overlay-active avia-bg-style-fixed    av-desktop-hide av-medium-hide container_wrap sidebar_right' style='background-repeat: no-repeat; background-image: url(https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/05\/wall005.jpg);background-attachment: fixed; background-position: bottom right;  '  data-section-bg-repeat='stretch' style='background-repeat: no-repeat; background-image: url(https:\/\/pairlabs.ai\/wp-content\/uploads\/2020\/05\/wall005.jpg);background-attachment: fixed; background-position: bottom right;  ' ><div class='av-section-color-overlay-wrap'><div class='av-section-color-overlay' style='opacity: 0.6; background-color: #ffffff; '><\/div><div class='container' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-4542'><div class='entry-content-wrapper clearfix'>\n<div class='flex_column_table av-equal-height-column-flextable -flextable' style='margin-top:0px; margin-bottom:-20px; '><div class=\"flex_column av_one_fourth  flex_column_table_cell av-equal-height-column av-align-top av-zero-column-padding first   \" style='border-radius:0px; '><\/div><\/div><!--close column table wrapper. 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Autoclose: 1 --><div class='flex_column_table av-equal-height-column-flextable -flextable' ><div class='av-flex-placeholder'><\/div><div class=\"flex_column av_one_half  flex_column_table_cell av-equal-height-column av-align-top    \" style='background-color:#00a0e9; background:linear-gradient(to bottom right,#00a0e9,#25a98f); padding:10px; border-radius:0px; '><p><div style=' margin-top:-21px; margin-bottom:0px;'  class='hr hr-custom hr-center hr-icon-yes   '><span class='hr-inner   inner-border-av-border-thin' style=' width:0px;' ><span class='hr-inner-style'><\/span><\/span><span class='av-seperator-icon' style='color:#ffffff;' aria-hidden='true' data-av_icon='\ue883' data-av_iconfont='entypo-fontello'><\/span><span class='hr-inner   inner-border-av-border-thin' style=' width:0px;' ><span class='hr-inner-style'><\/span><\/span><\/div><br \/>\n<section class=\"av_textblock_section \"  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div class='avia_textblock  av_inherit_color '  style='font-size:30px; color:#ffffff; '  itemprop=\"text\" ><h2 style=\"text-align: center;\">Computer games Team<\/h2>\n<\/div><\/section><\/p><\/div><\/div><!--close column table wrapper. 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Autoclose: 1 -->\n<\/div><\/div><\/div><!-- close content main div --><\/div><\/div><\/div><div id='after_section_2' class='main_color av_default_container_wrap container_wrap sidebar_right' style=' '   style=' ' ><div class='container' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-4542'><div class='entry-content-wrapper clearfix'>\n<\/div><\/div><\/div><!-- close content main div --><\/div><\/div><div id='av_section_3' class='avia-section socket_color avia-section-default avia-no-border-styling avia-bg-style-scroll    av-arrow-down-section container_wrap sidebar_right' style=' '   style=' ' ><div class='container' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-4542'><div class='entry-content-wrapper clearfix'>\n<div style='padding-bottom:0px; margin:0 0 0 0; font-size:30px;' class='av-special-heading av-special-heading-h3  blockquote modern-quote modern-centered   av-inherit-size '><h3 class='av-special-heading-tag '  itemprop=\"headline\"  >Studies of Applications with Deep Reinforcement Learning Technologies<\/h3><div class='special-heading-border'><div class='special-heading-inner-border' ><\/div><\/div><\/div>\n<\/div><\/div><\/div><!-- close content main div --><\/div><div class='av-extra-border-element border-extra-arrow-down'><div class='av-extra-border-outer'><div class='av-extra-border-inner'  style='background-color:#333333;' ><\/div><\/div><\/div><\/div><div id='after_section_3' class='main_color av_default_container_wrap container_wrap sidebar_right' style=' '   style=' ' ><div class='container' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-4542'><div class='entry-content-wrapper clearfix'>\n<\/div><\/div><\/div><!-- close content main div --><\/div><\/div><div id='av_section_4' class='avia-section main_color avia-section-default avia-no-border-styling avia-bg-style-scroll   container_wrap sidebar_right' style=' '   style=' ' ><div class='container' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-4542'><div class='entry-content-wrapper clearfix'>\n<div class=\"flex_column av_three_fifth  flex_column_div av-zero-column-padding first  \" style='border-radius:0px; '><section class=\"av_textblock_section \"  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div class='avia_textblock  '   itemprop=\"text\" ><p><b>Principal Investigator:<\/b><a href=\"http:\/\/pairlabs.ai\/\/en\/2018\/04\/20\/professor-i-chen-wu\/\">Professor I-Chen Wu<\/a><\/p>\n<p>&#8212;<\/p>\n<blockquote>\n<h5><b>Summary<\/b><\/h5>\n<\/blockquote>\n<p>Recently, Deep Reinforcement Learning (DRL) has been applied to many AI applications. One of the successful achievements is the AlphaGo Zero, called the Zero method in this project, was presented to learn Go playing without human knowledge and surprisingly surpass all the human players and all the AI programs. This project studies on five main topics for applications with Deep Reinforcement Learning: 1) Continue to research and develop our Go program CGI. 2) Apply the Zero method to other game AI. 3) Research on the combination of the Zero method with exact methods. 4) Research on the AI bot of video games. 5) Research on the random bin picking problem for robotic arms.<\/p>\n<blockquote>\n<h5><b>Keywords<\/b><\/h5>\n<\/blockquote>\n<p>Deep Reinforcement Learning, Reinforcement Learning, Deep Learning, Monte-Carlo Tree Search, AlphaGo Zero, Computer Games, Go, Video Games, Car Racing, Robotics, Random Bin Picking<\/p>\n<blockquote>\n<h5><b>Innovations<\/b><\/h5>\n<\/blockquote>\n<ul>\n<li>We propose a novel value network architecture, called a multi-labeled value network, which outputs values for different komi for the game Go, and also lowers the mean squared error.<\/li>\n<li>We propose an approach to strength adjustment for MCTS-based game-playing programs. And perform a theoretical analysis, reaching the result that the adjusted policy is guaranteed to choose moves exceeding a lower bound in strength by using a threshold ratio.<\/li>\n<li>We investigate whether the Zero method can also learn theoretical values and optimal plays for non-deterministic games, and develop the 2\u00d74 Chinese Dark Chess Zero program.<\/li>\n<li>We propose the hyperbolic-tangent decay, which can be applied with stochastic gradient descent.<\/li>\n<li>We propose a new method for state discretization, which can discretize the perception of environmental changes, and generate the state transition diagram.<\/li>\n<li>We propose a new weighted cross entropy method, which can achieve a success rate of nearly 100% in grasping tasks for robotic arms, while DDPG can only achieve 70%.<\/li>\n<li>We propose a new end-to-end hybrid action space DRL method, which can greatly improve the performance of grasping and pushing tasks for robotic arms.<\/li>\n<\/ul>\n<blockquote>\n<h5><b>Benefits<\/b><\/h5>\n<\/blockquote>\n<ul>\n<li>By combining the multi-labelled value network with Go programs, we develop the world\u2019s first Go Zero program that can play under different komis. The proposed method has also been published on the IEEE Transactions on Games.<\/li>\n<li>We develop a computer Go lifelong learning system which is the world\u2019s first Go system that is able to provide different strengths from beginners to super-humans. This result is selected for the show of the 2018 Future Tech. A paper for the strength adjustment has also been accepted by the top conference AAAI-19. (acceptance rate is only 1,150\/7,095 = 16.2%).<\/li>\n<li>The 2\u00d74 Chinese Dark Chess Zero program we developed is the first Zero program for stochastic games in the world. A paper for this also won the best paper award in TAAI 2018 conference.<\/li>\n<li>The paper of hyperbolic-tangent decay has been accepted by the IEEE WACV 2019 conference.<\/li>\n<li>The proposed distributed end-to-end DRL algorithm has been successfully applied to racing games in an industrial-university joint project.<\/li>\n<li>The new approach to state discretization is expected to be applied to many DRL applications.<\/li>\n<li>The end-to-end hybrid action space DRL method has been accepted by the Infer2Control workshop at the NIPS 2018 conference.<\/li>\n<\/ul>\n<\/div><\/section><\/div><div class=\"flex_column av_two_fifth  flex_column_div av-zero-column-padding   \" style='border-radius:0px; '><p><div class='avia-progress-bar-container  av-desktop-hide av-medium-hide av-small-hide av-mini-hide avia_animate_when_almost_visible   av-striped-bar av-animated-bar '><div class='avia-progress-bar theme-color-bar icon-bar-no'><div class='progressbar-title-wrap'><div 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