A Deep Reinforcement Learning based Homeostatic System for Unmanned Position Control
Priyanthi M. Dassanayake, Ashiq Anjum, Warren Manning, Craig Bower · 2019
Deep Reinforcement Learning (DRL) has been proven to be capable of designing an optimal control theory by minimising the error in dynamic systems. However, in many of the real-world operations, the exact behaviour of the environment is unknown. In such environments, random changes cause the system to reach different states for the same action. Hence, application of DRL for unpredictable environments is difficult as the states of the world cannot be known for non-stationary transition and reward functions.