Summary of Investigating the Impact Of Choice on Deep Reinforcement Learning For Space Controls, by Nathaniel Hamilton et al.
Investigating the Impact of Choice on Deep Reinforcement Learning for Space Controls
by Nathaniel Hamilton, Kyle Dunlap, Kerianne L. Hobbs
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Reinforcement Learning (RL) has been gaining popularity in autonomous space operations due to its promising performance across complex tasks. However, traditional RL agents learn bounded continuous control values, which may not be suitable for many space applications that prefer on/off control approaches. This paper explores using discrete action spaces, where the agent must choose from a predefined list of actions, and analyzes how the number of choices affects performance during and after training. The study focuses on two tasks: inspection and docking. For the inspection task, the results show that a limited number of discrete choices leads to optimal performance, while for the docking task, continuous control yields better results. The common objective of minimizing fuel usage motivates the agent to choose actions that use no fuel, highlighting the importance of autonomous operation in space applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using a new way to control robots and spacecraft called Reinforcement Learning (RL). RL helps machines make decisions without being told exactly what to do. Right now, most space missions use traditional methods for controlling their vehicles, but this can be time-consuming and limiting. The researchers in this study want to see if they can improve on this by giving the robots more choices about how to behave. They tested two different ways of doing this: one where the robot had a lot of options, and another where it only had a few. They found that for some tasks, like inspecting an object, having fewer options worked better, while for other tasks, like docking with another spacecraft, giving the robot more options was better. This research could help make space missions more efficient and autonomous in the future. |
Keywords
» Artificial intelligence » Reinforcement learning
