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. system state vector control action or input to the system state transition probabilities reward for taking action ‘a’ in state ‘s’ policy function or action to be taken in state ‘s’ cumulative discounted reward for following policy π starting from state ‘s’ optimal policy function optimal value function [h 1 h 2 h 3]T, state vector for the liquid level system inlet flow rate for the liquid level system discount factor to discount future rewards and favor immediate rewards number of discretization levels used for level variable h i number of discretization levels used for inlet flow rate Q 
Control of a nonlinear liquid level system using a new artificial neural network based reinforcement learning approach