Controlling dynamics of stochastic systems with deep reinforcement learning
This work addresses the gap between control theory and deep reinforcement learning for stochastic systems, offering a general simulation scheme that could benefit researchers in physics and engineering.
The paper tackled the problem of controlling stochastic systems by proposing a simulation algorithm that uses deep reinforcement learning with artificial neural networks as controllers, demonstrating effectiveness on particle coalescence and a totally asymmetric exclusion process.
A properly designed controller can help improve the quality of experimental measurements or force a dynamical system to follow a completely new time-evolution path. Recent developments in deep reinforcement learning have made steep advances toward designing effective control schemes for fairly complex systems. However, a general simulation scheme that employs deep reinforcement learning for exerting control in stochastic systems is yet to be established. In this paper, we attempt to further bridge a gap between control theory and deep reinforcement learning by proposing a simulation algorithm that allows achieving control of the dynamics of stochastic systems through the use of trained artificial neural networks. Specifically, we use agent-based simulations where the neural network plays the role of the controller that drives local state-to-state transitions. We demonstrate the workflow and the effectiveness of the proposed control methods by considering the following two stochastic processes: particle coalescence on a lattice and a totally asymmetric exclusion process.