Combining Reinforcement Learning with Arc-search Interior-Point Method for Path Planning

Abstract

Path planning in environments containing obstacles has numerous practical applications. The problem is challenging because it is inherently nonlinear and nonconvex. Consequently, a variety of techniques have been developed to address this problem, among which machine learning and optimal control (or optimization) have emerged as two prominent approaches. In general, machine learning methods do not require a high-fidelity model, and a trained agent can often generate a feasible path in real time. However, the resulting path is not necessarily optimal with respect to performance objectives such as minimizing path length or travel time. In contrast, optimal control and optimization methods typically rely on high-fidelity models and often require computational effort that may not satisfy real-time constraints. Nevertheless, these methods are more likely to produce optimal or near-optimal solutions. To overcome the limitations of each approach while exploiting their respective strengths, this paper proposes a framework that combines reinforcement learning with an arc-search interior-point method for path planning. Numerical simulations demonstrate that the proposed approach effectively integrates the real-time decision-making capability of reinforcement learning with the optimization performance of the arc-search interior-point method, resulting in improved path-planning performance.