Mobile robot control by neural networks using self-supervised learning
A reinforcement learning algorithm based on supervised learning is described. It uses associative search to discover and learn actions that make the system perform a desired task. One problem with associative search is that the system's actions are often inconsistent. In the searching process,...
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| Veröffentlicht in: | IEEE transactions on industrial electronics (1982) 1992-12, Vol.39 (6), p.537-542 |
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| container_end_page | 542 |
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| container_issue | 6 |
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| container_title | IEEE transactions on industrial electronics (1982) |
| container_volume | 39 |
| creator | Saga, K. Sugasaka, T. Sekiguchi, M. Nagata, S. Asakawa, K. |
| description | A reinforcement learning algorithm based on supervised learning is described. It uses associative search to discover and learn actions that make the system perform a desired task. One problem with associative search is that the system's actions are often inconsistent. In the searching process, the system's actions are always decided stochastically, so the system cannot perform learned actions more than once, even if they have been determined to be suitable actions for the desired task. To solve this problem, a neural network that can predict an evaluation of an action and control the influence of the stochastic element is used. Results from computer simulations using the algorithms to control a mobile robot are described.< > |
| doi_str_mv | 10.1109/41.170973 |
| format | Article |
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It uses associative search to discover and learn actions that make the system perform a desired task. One problem with associative search is that the system's actions are often inconsistent. In the searching process, the system's actions are always decided stochastically, so the system cannot perform learned actions more than once, even if they have been determined to be suitable actions for the desired task. To solve this problem, a neural network that can predict an evaluation of an action and control the influence of the stochastic element is used. Results from computer simulations using the algorithms to control a mobile robot are described.< ></description><subject>Applied sciences</subject><subject>Computer science; control theory; systems</subject><subject>Control systems</subject><subject>Control theory. Systems</subject><subject>Error correction</subject><subject>Exact sciences and technology</subject><subject>Mobile robots</subject><subject>Neural networks</subject><subject>Noise generators</subject><subject>Performance evaluation</subject><subject>Robot control</subject><subject>Robotics</subject><subject>Stochastic systems</subject><subject>Supervised learning</subject><subject>Training data</subject><issn>0278-0046</issn><issn>1557-9948</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1992</creationdate><recordtype>article</recordtype><recordid>eNqFkDFPwzAQRi0EEqEwsDJlQEgMKXbsxPaIKgpIRSwwR058RgE3Lr4E1H9PqlQwcss33LunuyPknNE5Y1TfCDZnkmrJD0jCikJmWgt1SBKaS5VRKspjcoL4TikTBSsSsnwKdeshjaEOfdqEro_Bp_U27WCIxo_Rf4f4gemAbfeWIniX4bCB-NUi2NSDid3YOCVHzniEs33OyOvy7mXxkK2e7x8Xt6us4Vz2mRWitmVprXCGWalFXqragCx4LSxVSkmolVHMcVBgHOW6KXJrwDo5HsUon5GrybuJ4XMA7Kt1iw14bzoIA1a55kKXQv4PKi5EqfMRvJ7AJgbECK7axHZt4rZitNq9tBKsml46spd7qcHGeBdN17T4OyDGysvdkhcT1gLAn25y_ACUE35C</recordid><startdate>19921201</startdate><enddate>19921201</enddate><creator>Saga, K.</creator><creator>Sugasaka, T.</creator><creator>Sekiguchi, M.</creator><creator>Nagata, S.</creator><creator>Asakawa, K.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><scope>7TB</scope><scope>FR3</scope></search><sort><creationdate>19921201</creationdate><title>Mobile robot control by neural networks using self-supervised learning</title><author>Saga, K. ; Sugasaka, T. ; Sekiguchi, M. ; Nagata, S. ; Asakawa, K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c337t-d44bd66dd4fa1d794268bae753b4d08887eb8a81f3e8eaf039c52daedf7709103</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1992</creationdate><topic>Applied sciences</topic><topic>Computer science; control theory; systems</topic><topic>Control systems</topic><topic>Control theory. Systems</topic><topic>Error correction</topic><topic>Exact sciences and technology</topic><topic>Mobile robots</topic><topic>Neural networks</topic><topic>Noise generators</topic><topic>Performance evaluation</topic><topic>Robot control</topic><topic>Robotics</topic><topic>Stochastic systems</topic><topic>Supervised learning</topic><topic>Training data</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Saga, K.</creatorcontrib><creatorcontrib>Sugasaka, T.</creatorcontrib><creatorcontrib>Sekiguchi, M.</creatorcontrib><creatorcontrib>Nagata, S.</creatorcontrib><creatorcontrib>Asakawa, K.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on industrial electronics (1982)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Saga, K.</au><au>Sugasaka, T.</au><au>Sekiguchi, M.</au><au>Nagata, S.</au><au>Asakawa, K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mobile robot control by neural networks using self-supervised learning</atitle><jtitle>IEEE transactions on industrial electronics (1982)</jtitle><stitle>TIE</stitle><date>1992-12-01</date><risdate>1992</risdate><volume>39</volume><issue>6</issue><spage>537</spage><epage>542</epage><pages>537-542</pages><issn>0278-0046</issn><eissn>1557-9948</eissn><coden>ITIED6</coden><abstract>A reinforcement learning algorithm based on supervised learning is described. It uses associative search to discover and learn actions that make the system perform a desired task. One problem with associative search is that the system's actions are often inconsistent. In the searching process, the system's actions are always decided stochastically, so the system cannot perform learned actions more than once, even if they have been determined to be suitable actions for the desired task. To solve this problem, a neural network that can predict an evaluation of an action and control the influence of the stochastic element is used. Results from computer simulations using the algorithms to control a mobile robot are described.< ></abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/41.170973</doi><tpages>6</tpages></addata></record> |
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| ispartof | IEEE transactions on industrial electronics (1982), 1992-12, Vol.39 (6), p.537-542 |
| issn | 0278-0046 1557-9948 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Applied sciences Computer science control theory systems Control systems Control theory. Systems Error correction Exact sciences and technology Mobile robots Neural networks Noise generators Performance evaluation Robot control Robotics Stochastic systems Supervised learning Training data |
| title | Mobile robot control by neural networks using self-supervised learning |
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