473188 Deep Reinforcement Learning Approach for Process Control
natural language processing that followed the success of deep learning . Human level control
has been attained in games  and physical tasks  by combining deep learning with
reinforcement learning resulting in 'Deep Q Network' . In the process industry, Model
Predictive Control (MPC) has been found to be an effective control strategy. However,
application of MPC on nonlinear stochastic systems can be computationally demanding and may
require estimation of hidden states in a complex system. The performance of MPC depends
significantly on the quality of model and hence any Model Plant Mismatch (MPM) would be
detrimental to the performance. In this work, we use deep learning and reinforcement learning
for controlling process variables. We present an actor-critic based approach to deterministic policy gradient
reinforcement learning algorithm for control. During the training phase of the learning algorithm,
we consider the standard reinforcement learning setup, where an agent (controller) interacts with
an environment (process) through control actions and receives a reward in discrete time steps.
The training is done with full state observation. Deep neural networks serve as function
approximators and are used to learn the control policies. Once trained, the learned network
acquires a policy that maps system output to control actions and it can be used to control the
plant without full state observations. Since online optimization is not required as in MPC our
algorithm is computationally less intensive during online operation phase. We evaluated our
approach on Single Input Single Output Systems and Multiple Input Multiple Output Systems
with varying set points and initial conditions and compared it with MPC.
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International Conference on Learning Representations, 2016.