This paper presents a reinforcement-learning (RL)-based robust low-thrust guidance
method for asteroid approaching under process uncertainties. Markov decision processes …

Multi-objective reinforcement learning is used to uncover a subset of the multi-objective
solution space for low-thrust transfers between two L 2 southern halo orbits in the Earth …

To overcome the time-consuming training caused by the sparse reward function in
reinforcement learning, an efficient dense reward framework for robust low-thrust trajectory …

This paper investigates the use of reinforcement learning for the fuel-optimal guidance of a
spacecraft during a time-free low-thrust transfer between two libration point orbits in the …

A reinforcement learning-based approach is proposed to design the multi-impulse
rendezvous trajectories in linear relative motions. For the relative motion in elliptical orbits …

Deep space missions, and particularly cislunar endeavors, are becoming a major field of
interest for the space industry, including for the astrodynamics research community. While …

This paper presents a reinforcement learning (RL) based approach for autonomous
maneuver planning of low-altitude flybys for site-specific reconnaissance of small bodies …

The recent advances in space technology are focusing on fully autonomous, real-time, long-
term orbit management and mission planning for large-scale satellite constellations in Low …

Many far-reaching objectives and aspirations in space exploration are predicated on
achieving a high degree of autonomous functionality. Traditional Earth-based mission …

Innovative lightweight and model-free guidance algorithms are essential to achieve full
autonomy of spacecraft and address future space exploration challenges. In the near future …