  For many space applications, traditional control methods are often used
during operation. However, as the number of space assets continues to grow,
autonomous operation can enable rapid development of control methods for
different space related tasks. One method of developing autonomous control is
Reinforcement Learning (RL), which has become increasingly popular after
demonstrating promising performance and success across many complex tasks.
While it is common for RL agents to learn bounded continuous control values,
this may not be realistic or practical for many space tasks that traditionally
prefer an on/off approach for control. This paper analyzes using discrete
action spaces, where the agent must choose from a predefined list of actions.
The experiments explore how the number of choices provided to the agents
affects their measured performance during and after training. This analysis is
conducted for an inspection task, where the agent must circumnavigate an object
to inspect points on its surface, and a docking task, where the agent must move
into proximity of another spacecraft and "dock" with a low relative speed. A
common objective of both tasks, and most space tasks in general, is to minimize
fuel usage, which motivates the agent to regularly choose an action that uses
no fuel. Our results show that a limited number of discrete choices leads to
optimal performance for the inspection task, while continuous control leads to
optimal performance for the docking task.
