Summary of Tradeoffs When Considering Deep Reinforcement Learning For Contingency Management in Advanced Air Mobility, by Luis E. Alvarez and Marc W. Brittain and Steven D. Young
Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility
by Luis E. Alvarez, Marc W. Brittain, Steven D. Young
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 A novel application of Deep Reinforcement Learning (DRL) in Advanced Air Mobility (AAM) is proposed to address the increased complexity and heterogeneity of vehicle capabilities and density. The use of DRL in a Markov Decision Process (MDP) framework is explored for training agents that mitigate hazards in the simulation environment. This approach has shown promising performance in complex, high-dimensional environments where sequential decision-making is required. The paper presents a comparison of learning-based agents and classical techniques in terms of their performance, verification difficulties, and development process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Air transportation is changing fast with new flying cars coming out! To keep people safe and planes running smoothly, computers might need to take control sometimes. This paper talks about how we can use special computer programs called Deep Reinforcement Learning (DRL) to help pilots make good decisions quickly in changing situations. It’s like a video game where the computer learns from its mistakes to avoid hazards and stay on track. |
Keywords
* Artificial intelligence * Reinforcement learning
