BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:US/Eastern BEGIN:DAYLIGHT DTSTART:20250309T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20251102T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260124T053957Z UID:69C276A1-C54F-4A9F-90B1-A3510D3C890F DTSTART;TZID=US/Eastern:20250423T183000 DTEND;TZID=US/Eastern:20250423T210000 DESCRIPTION:AI for Control: Reinforcement Learning Short Course\n\nReinforc ement learning\, a subset of artificial intelligence\, is a computational approach that models decision-making by exploring the cause-and-effect rel ationships between actions and rewards. It provides a framework for solvin g optimization problems where an agent interacts with its environment and refines its policies over time. Closely related to both optimal and adapti ve control\, reinforcement learning has significant applications in contro l systems. This study explores the fundamental principles of reinforcement learning and its integration into control applications.\n\nUpon completio n of this program\, you'll walk away with:\n\n- A solid understanding of r einforcement learning fundamentals as a subset of artificial intelligence\ n- The relationship between artificial intelligence\, reinforcement learni ng\, and optimal control.\n- Practical skills in designing\, training\, an d deploying reinforcement learning-based controllers for dynamic systems.\ n- The ability to understand the value-based reinforcement learning method s for optimization.\n- The confidence to apply reinforcement learning tech niques to real-world control problems in robotics\, automation\, and beyon d.\n\nCo-sponsored by: IEEE CSS/CASS/SMCS Philadelphia Chapter\n\nSpeaker( s): Chang-hee Won\, PhD\n\nAgenda: \nWEBINAR: 6:30 - 9:00 P.M.\n\nThe Zoom Webinar link and password will be forwarded to all registered participant s after Noon on the day of the meeting. Check your spam folder if you don' t see the email.\n\nPDH certificates are available and an evaluation form will be emailed to you after the meeting. PDH certificate are sent by IEEE USA 3-4 weeks after the meeting.\n\nVirtual: https://events.vtools.ieee.o rg/m/467824 LOCATION:Virtual: https://events.vtools.ieee.org/m/467824 ORGANIZER:ieee@gpamg.org SEQUENCE:40 SUMMARY:AI for Control: Reinforcement Learning Short Course URL;VALUE=URI:https://events.vtools.ieee.org/m/467824 X-ALT-DESC:Description: <br /><p><strong>AI for Control: Reinforcement Lear ning Short Course&nbsp\;</strong></p>\n<p class="MsoNormal" style="mso-lay out-grid-align: none\; text-autospace: none\;"><span style="font-size: 12. 0pt\; line-height: 115%\; font-family: 'Arial'\,sans-serif\;">Reinforcemen t learning\, a subset of artificial intelligence\, is a computational appr oach that models decision-making by exploring the cause-and-effect relatio nships between actions and rewards. It provides a framework for solving op timization problems where an agent interacts with its environment and refi nes its policies over time. Closely related to both optimal and adaptive c ontrol\, reinforcement learning has significant applications in control sy stems. This study explores the fundamental principles of reinforcement lea rning and its integration into control applications.</span></p>\n<p class= "MsoNormal"><strong><span style="font-size: 14.0pt\; font-family: 'Calibri '\,sans-serif\;">Upon completion of this program\, you'll walk away with:< /span></strong></p>\n<ol style="margin-top: 0in\;" start="1" type="1">\n<l i class="MsoNormal" style="mso-list: l0 level1 lfo1\; tab-stops: list .5in \;"><span style="font-family: 'Arial'\,sans-serif\; mso-fareast-font-famil y: 'Times New Roman'\;">A solid understanding of reinforcement learning fu ndamentals as a subset of artificial intelligence</span></li>\n<li class=" MsoNormal" style="mso-list: l0 level1 lfo1\; tab-stops: list .5in\;"><span style="font-family: 'Arial'\,sans-serif\; mso-fareast-font-family: 'Times New Roman'\;">The relationship between artificial intelligence\, reinforc ement learning\, and optimal control.</span></li>\n<li class="MsoNormal" s tyle="mso-list: l0 level1 lfo1\; tab-stops: list .5in\;"><span style="font -family: 'Arial'\,sans-serif\; mso-fareast-font-family: 'Times New Roman'\ ;">Practical skills in designing\, training\, and deploying reinforcement learning-based controllers for dynamic systems.</span></li>\n<li class="Ms oNormal" style="mso-list: l0 level1 lfo1\; tab-stops: list .5in\;"><span s tyle="font-family: 'Arial'\,sans-serif\; mso-fareast-font-family: 'Times N ew Roman'\;">The ability to understand the value-based reinforcement learn ing methods for optimization.</span></li>\n<li class="MsoNormal" style="ms o-list: l0 level1 lfo1\; tab-stops: list .5in\;"><span style="font-family: 'Arial'\,sans-serif\; mso-fareast-font-family: 'Times New Roman'\;">The c onfidence to apply reinforcement learning techniques to real-world control problems in robotics\, automation\, and beyond.</span></li>\n</ol><br />< br />Agenda: <br /><p><strong>WEBINAR:</strong> 6:30 - 9:00 P.M.&nbsp\;</p >\n<p>The Zoom Webinar link and password will be forwarded to all register ed participants after Noon on the day of the meeting. <strong>Check your s pam folder if you don't see the email.&nbsp\;</strong></p>\n<p>PDH certifi cates are available and an evaluation form will be emailed to you after th e meeting. PDH certificate are sent by IEEE USA 3-4 weeks after the meetin g.</p> END:VEVENT END:VCALENDAR