A Novel Approach to Feedback Control with Deep Reinforcement Learning
Yuan Wang, Kirubakaran Velswamy, Biao Huang · IFAC-PapersOnLine · 2018
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.