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A Novel Approach to Feedback Control with Deep Reinforcement Learning - 2018

Research Area:  Machine Learning

Abstract:

A novel deep reinforcement learning (RL) algorithm is applied for feedback control application. We propose Proximal Actor-Critic, a model-free reinforcement learning algorithm that can learn robust feedback control laws from direct interaction data from the plant. We show efficacy of the algorithm on a benchmark problem in Heating Ventilation and Air Conditioning (HVAC) heating system, with the RL controller achieving lower Integral Absolute Error (IAE) and Integral Square Error(ISE) as compared to baseline Proportional-Integral (PI) and Linear Quadratic Regulator (LQR) controllers. We also provide details on establishing feedback control problems within the deep reinforcement learning framework, including policy parameterization, neural network architecture and training procedures.

Author(s) Name:  Yuan Wang, Kirubakaran Velswamy, Biao Huang

Journal name:  IFAC-Papers OnLine

Conferrence name:  

Publisher name:  Elsevier

DOI:  10.1016/j.ifacol.2018.09.241

Volume Information:  Volume 51, Issue 18, 2018, Pages 31-36