Summary of Guidance Design For Escape Flight Vehicle Using Evolution Strategy Enhanced Deep Reinforcement Learning, by Xiao Hu et al.
Guidance Design for Escape Flight Vehicle Using Evolution Strategy Enhanced Deep Reinforcement Learning
by Xiao Hu, Tianshu Wang, Min Gong, Shaoshi Yang
First submitted to arxiv on: 4 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); 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 In this paper, researchers explore guidance commands for flight vehicles using deep reinforcement learning (DRL). The study focuses on escape and pursuit scenarios, where the objective is to maximize residual velocity while adhering to evasion distance constraints. A two-step strategy is proposed: first, proximal policy optimization (PPO) generates guidance commands, followed by an evolution strategy (ES)-based algorithm to refine results. Simulation outcomes show that PPO-based guidance design achieves a residual velocity of 67.24 m/s, outperforming benchmarks like soft actor-critic and deep deterministic policy gradient algorithms. The proposed ES-enhanced PPO algorithm further improves this result, reaching a residual velocity of 69.04 m/s. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses special computer learning to help flight vehicles navigate safely. Imagine you’re in charge of a plane trying to get away from another plane that’s chasing it! You need to make decisions quickly and carefully to stay safe. This research uses special algorithms to figure out the best way for the “chasing” plane to follow, while also making sure the “escaping” plane has enough time to get away. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
