用深度强化学习控制随机系统的演化路径。
Controlling dynamics of stochastic systems with deep reinforcement learning
- 用神经网络作为控制器，驱动系统状态转移。
- 在格点粒子凝聚和排斥过程中实现有效控制。
- 适合研究复杂随机系统控制的学者参考。
合适的控制器可提升实验测量质量或引导动力系统沿全新时间演化路径运行。深度强化学习在复杂系统控制方案设计方面取得显著进展，但针对随机系统的通用仿真控制框架仍不完善。本文通过提出一种基于智能体的仿真算法，将训练好的神经网络作为控制器，实现对随机系统动力学的调控。具体而言，神经网络作为控制器，决定系统局部状态间的转移。我们以格点上的粒子凝聚过程和完全非对称排斥过程为例，验证了该方法的可行性和有效性。
原文摘要 · Abstract (English)
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.
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