Reinforcement Learning Control

Control of a nonlinear liquid level system using a new artificial neural network based reinforcement learning approach

Base paper (published in The Applied Soft Computing journal):

Mathew Mithra Noel, B. Jaganatha Pandian, Control of a nonlinear liquid level system using a new artificial neural network based reinforcement learning approach,
Applied Soft Computing, Volume 23, 2014, Pages 444-451, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2014.06.037.
(http://www.sciencedirect.com/science/article/pii/S1568494614003111)

Code demonstrates RL control of a complex nonlinear system. The state space model of the interacting two-tank liquid level system can be replaced by the state space model of a different system and the same code can be tuned to control other nonlinear systems.

Most industrial processes exhibit inherent nonlinear characteristics. Hence, classical control strategies which use linearized models are not effective in achieving optimal control. In this paper an Artificial Neural Network (ANN) based reinforcement learning (RL) strategy is proposed for controlling a nonlinear interacting liquid level system. This ANN-RL control strategy takes advantage of the generalization, noise immunity and function approximation capabilities of the ANN and optimal decision making capabilities of the RL approach. Two different ANN-RL approaches for solving a generic nonlinear control problem are proposed and their performances are evaluated by applying them to two benchmark nonlinear liquid level control problems. Comparison of the ANN-RL approach is also made to a discretized state space based pure RL control strategy. Performance comparison on the benchmark nonlinear liquid level control problems indicate that the ANN-RL approach results in better control as evidenced by less oscillations,disturbance rejection and overshoot.