Relative motion guidance for near-rectilinear lunar orbits with path constraints via actor-critic reinforcement learning

This paper presents a feedback guidance algorithm for proximity operation in cislunar environment based on actor-critic reinforce-ment learning. The algorithm is lightweight, closed-loop, and capable of taking path constraints into account. The method relies on rein-forcement learning to make the well known Zero-Effort-Miss/Zero-Effort-Velocity guidance state dependent and allow for path constraints to be directly embedded. The algorithm is tested in the circular restricted three-body problem (CRTBP) framework for Near Rectilinear Orbits (NRO) in the Earth-Moon system. It shows promising results in terminal guidance error and satisfies path constraints in constraint scenarios comprising spherical constraints and keep-out-spheres with approach corridors. Furthermore, this approach indi-cates that reinforcement learning can be effectively used to solve constrained relative spacecraft guidance problems in complex environ-ments and thus can be effective for autonomous relative motion operations in the Earth-Moon dynamical environment.