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ÇѱÛÁ¦¸ñ(Korean Title) |
½º¸¶Æ® ÆÑÅ丮¿¡¼ ±×¸®µå ºÐ·ù ½Ã½ºÅÛÀÇ Çù·ÂÀû ´ÙÁß ¿¡ÀÌÀüÆ® °È ÇнÀ ±â¹Ý Çൿ Á¦¾î |
¿µ¹®Á¦¸ñ(English Title) |
Cooperative Multi-Agent Reinforcement Learning-Based Behavior Control of Grid Sortation Systems in Smart Factory |
ÀúÀÚ(Author) |
HoBin Choi
JuBong Kim
GyuYoung Hwang
KwiHoon Kim
YongGeun Hong
YounHee Han
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¿ø¹®¼ö·Ïó(Citation) |
VOL 09 NO. 08 PP. 0171 ~ 0180 (2020. 08) |
Çѱ۳»¿ë (Korean Abstract) |
½º¸¶Æ® ÆÑÅ丮´Â ¼³°è, °³¹ß, Á¦Á¶ ¹× À¯Åë µî »ý»ê°úÁ¤ Àü¹ÝÀÌ µðÁöÅÐ ÀÚµ¿È ¼Ö·ç¼ÇÀ¸·Î ÀÌ·ç¾îÁ® ÀÖÀ¸¸ç, ³»ºÎ ¼³ºñ¿Í ±â°è¿¡ »ç¹°ÀÎÅͳÝ(IoT)À» ¼³Ä¡ÇØ °øÁ¤ µ¥ÀÌÅ͸¦ ½Ç½Ã°£À¸·Î ¼öÁýÇÏ°í À̸¦ ºÐ¼®ÇØ ½º½º·Î Á¦¾îÇÒ ¼ö ÀÖ°Ô ÇÏ´Â Áö´ÉÇü °øÀåÀÌ´Ù. ½º¸¶Æ® ÆÑÅ丮ÀÇ ÀåºñµéÀº °ÔÀÓ°ú °°ÀÌ °¡»óÀÇ Ä³¸¯ÅÍ°¡ ÇϳªÀÇ °´Ã¼ ´ÜÀ§·Î ±¸µ¿µÇ´Â °ÍÀÌ ¾Æ´Ï¶ó ¼ö¸¹Àº Çϵå¿þ¾î°¡ ¹°¸®ÀûÀ¸·Î Á¶ÇÕµÇ¾î ¿¬µ¿ÇÑ´Ù. Áï, ƯÁ¤ÇÑ °øµ¿ÀÇ ¸ñÇ¥¸¦ À§ÇØ ´Ù¼öÀÇ ÀåÄ¡°¡ °³º°ÀûÀÎ ÇൿÀ» µ¿½Ã´Ù¹ßÀûÀ¸·Î ¼öÇàÇØ¾ß ÇÑ´Ù. °øÁ¤ µ¥ÀÌÅ͸¦ ½Ç½Ã°£À¸·Î ¼öÁýÇÒ ¼ö ÀÖ´Â ½º¸¶Æ® ÆÑÅ丮ÀÇ ÀåÁ¡À» È°¿ëÇÏ¿©, ÀϹÝÀûÀÎ ±â°è ÇнÀÀÌ ¾Æ´Ñ °È ÇнÀÀ» »ç¿ëÇÏ¸é ¹Ì¸® ¿ä±¸µÇ´Â ÈÆ·Ã µ¥ÀÌÅÍ ¾øÀÌ Çൿ Á¦¾î¸¦ ÇÒ ¼ö ÀÖ´Ù. ÇÏÁö¸¸, Çö½Ç ¼¼°è¿¡¼´Â ¹°¸®Àû ¸¶¸ð, ½Ã°£Àû ¹®Á¦ µîÀ¸·Î ÀÎÇØ ¼öõ¸¸ ¹ø ÀÌ»óÀÇ ¹Ýº¹ ÇнÀÀÌ ºÒ°¡´ÉÇÏ´Ù. µû¶ó¼, º» ³í¹®¿¡¼´Â ½Ã¹Ä·¹ÀÌÅ͸¦ È°¿ëÇØ ½º¸¶Æ® ÆÑÅ丮 ºÐ¾ß¿¡¼ º¹ÀâÇÑ È¯°æ Áß ÇϳªÀÎ ÀÌ¼Û ¼³ºñ¿¡ ÃÊÁ¡À» µÐ ±×¸®µå ºÐ·ù ½Ã½ºÅÛÀ» °³¹ßÇÏ°í Çù·ÂÀû ´ÙÁß ¿¡ÀÌÀüÆ® ±â¹ÝÀÇ °È ÇнÀÀ» ¼³°èÇÏ¿© È¿À²ÀûÀÎ Çൿ Á¦¾î°¡ °¡´ÉÇÔÀ» ÀÔÁõÇÑ´Ù.
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¿µ¹®³»¿ë (English Abstract) |
Smart Factory consists of digital automation solutions throughout the production process, including design, development, manufacturing and distribution, and it is an intelligent factory that installs IoT in its internal facilities and machines to collect process data in real time and analyze them so that it can control itself. The smart factory's equipment works in a physical combination of numerous hardware, rather than a virtual character being driven by a single object, such as a game. In other words, for a specific common goal, multiple devices must perform individual actions simultaneously. By taking advantage of the smart factory, which can collect process data in real time, if reinforcement learning is used instead of general machine learning, behavior control can be performed without the required training data. However, in the real world, it is impossible to learn more than tens of millions of iterations due to physical wear and time. Thus, this paper uses simulators to develop grid sortation systems focusing on transport facilities, one of the complex environments in smart factory field, and design cooperative multi-agent-based reinforcement learning to demonstrate efficient behavior control.
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Å°¿öµå(Keyword) |
Deep Learning
Reinforcement Learning
Sortation System
Cooperative Multi-Agent
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