Understanding the transition events between metastable states in complex
systems is an important subject in the fields of computational physics,
chemistry and biology. The transition pathway plays an important role in
characterizing the mechanism underlying the transition, for example, in the
study of conformational changes of bio-molecules. In fact, computing the
transition pathway is a challenging task for complex and high-dimensional
systems. In this work, we formulate the path-finding task as a cost
minimization problem over a particular path space. The cost function is adapted
from the Freidlin-Wentzell action functional so that it is able to deal with
rough potential landscapes. The path-finding problem is then solved using a
actor-critic method based on the deep deterministic policy gradient algorithm
(DDPG). The method incorporates the potential force of the system in the policy
for generating episodes and combines physical properties of the system with the
learning process for molecular systems. The exploitation and exploration nature
of reinforcement learning enables the method to efficiently sample the
transition events and compute the globally optimal transition pathway. We
illustrate the effectiveness of the proposed method using three benchmark
systems including an extended Mueller system and the Lennard-Jones system of
seven particles.

在计算物理、化学和生物学领域中，了解复杂系统中亚稳态之间的转变事件是重要的课题。本文将路径查找任务作为特定路径空间上的成本最小化问题进行了阐述，并利用深度确定性策略梯度算法（DDPG）中的演员 - 评论者方法来解决该问题。所提出的方法利用增强学习的开发和探索性质来高效采样转变事件并计算全局最优的转变路径。通过对包括扩展的 Mueller 系统和七粒子 Lennard-Jones 系统在内的三个基准系统的实证，我们展示了所提出方法的有效性。