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DESCRIPTION:Abstract: In this presentation\, I will provide an overview of 
 reinforcement learning (RL) and focus on the value function approximation-
 based method for nonlinear process control and real time optimization (RTO
 ) under uncertainties. In the first part\, the traditional model predictiv
 e control (MPC) and RL are well-integrated to achieve offset-free control 
 with fast online computations. In the second part\, a data-driven RL schem
 e is specifically designed for determining the optimal operating condition
 s in large-scale and risk-sensitive systems\, such as refinery\, where ope
 rational risks should be minimized and revenue should be maximized. Severa
 l benchmark problems are solved to demonstrate the effectiveness of the pr
 oposed RL approaches.\n\nSpeaker(s): Yu Yang\, \n\nVirtual: https://events
 .vtools.ieee.org/m/361972
LOCATION:Virtual: https://events.vtools.ieee.org/m/361972
ORGANIZER:henry.yeh@csulb.edu
SEQUENCE:1
SUMMARY: Reinforcement Learning for Nonlinear Process Control and Optimizat
 ion Under Uncertainties
URL;VALUE=URI:https://events.vtools.ieee.org/m/361972
X-ALT-DESC:Description: <br /><div class="page" title="Page 1">\n<div class
 ="layoutArea">\n<div class="column">\n<p>Abstract: In this presentation\, 
 I will provide an overview of reinforcement learning (RL) and focus on the
  value function approximation-based method for nonlinear process control a
 nd real time optimization (RTO) under uncertainties. In the first part\, t
 he traditional model predictive control (MPC) and RL are well-integrated t
 o achieve offset-free control with fast online computations. In the second
  part\, a data-driven RL scheme is specifically designed for determining t
 he optimal operating conditions in large-scale and risk-sensitive systems\
 , such as refinery\, where operational risks should be minimized and reven
 ue should be maximized. Several benchmark problems are solved to demonstra
 te the effectiveness of the proposed RL approaches.</p>\n</div>\n</div>\n<
 /div>
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