# Investigating the Impact of Choice on Deep Reinforcement Learning for Space Controls

arXiv: https://arxiv.org/abs/2405.12355 · HTML: https://mlsanity.com/paper/2405.12355/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 20 May 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Deep Reinforcement Learning at the Edge of the Statistical Precipice](https://mlsanity.com/paper/2108.13264/) (2021)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [Safe Reinforcement Learning via Shielding](https://mlsanity.com/paper/1708.08611/) (2017)
- [A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems](https://mlsanity.com/paper/1705.01292/) (2017)
- [Simulation to Scaled City: Zero-Shot Policy Transfer for Traffic Control via Autonomous Vehicles](https://mlsanity.com/paper/1812.06120/) (2018)
- [Deep Reinforcement Learning for Autonomous Spacecraft Inspection using Illumination](https://mlsanity.com/paper/2308.02743/) (2023)
- [Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agents](https://mlsanity.com/paper/2207.04117/) (2022)
- [Exposure-Based Multi-Agent Inspection of a Tumbling Target Using Deep Reinforcement Learning](https://mlsanity.com/paper/2302.14188/) (2023)

## Abstract (arXiv)

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.