Deep Reinforcement Learning for the Automatic Six-Degree-of-Freedom Docking Maneuver of Space Vehicles

The implementation of a model-free, off-policy, actor-critic deep reinforcement learning algorithm consistent of two separate agents to a six-degree-of freedom spacecraft docking maneuver to develop a control policy is carried out in the research presented in this article. Reinforcement learning has the ability to learn without instruction, this aspect provides a potential framework for autonomous docking maneuvers in uncertain environments with low on-board computational cost. A Twin-Delayed Deep Deterministic Policy Gradient algorithm consistent of two agents is used to synthesise the docking control policy valid for the six degree-of-freedom continuous state-space. Testing of the resultant policy exhibits its behaviour and capability to achieve successful docking within the established position and attitude ranges.
Aerospace Engineering