Improved Long Short-Term Memory-based Wastewater Treatment Simulators for Deep Reinforcement Learning

arXiv:2403.15091v1 Announce Type: new
Abstract: Even though Deep Reinforcement Learning (DRL) showed outstanding results in the fields of Robotics and Games, it is still challenging to implement it in the optimization of industrial processes like wastewater treatment. One of the challenges is the lack of a simulation environment that will represent the actual plant as accurately as possible to train DRL policies. Stochasticity and non-linearity of wastewater treatment data lead to unstable and incorrect predictions of models over long time …

abstract arxiv challenges cs.ai cs.lg cs.sy eess.sy environment fields games industrial long short-term memory memory optimization processes reinforcement reinforcement learning results robotics simulation treatment type wastewater