Summary of Deceptive Path Planning Via Reinforcement Learning with Graph Neural Networks, by Michael Y. Fatemi and Wesley A. Suttle and Brian M. Sadler
Deceptive Path Planning via Reinforcement Learning with Graph Neural Networks
by Michael Y. Fatemi, Wesley A. Suttle, Brian M. Sadler
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper tackles deceptive path planning (DPP), a challenging problem where agents design paths that conceal their true goals from observers. Existing methods rely on unrealistic assumptions and are often specific to individual problems, hindering scalability and adaptability. The authors propose a reinforcement learning-based approach to overcome these limitations, introducing local perception models, new state spaces, graph neural networks, and deception bonuses. Their scheme trains policies to perform DPP over arbitrary weighted graphs, achieving generalization, scaling, tunable levels of deception, and real-time adaptation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create paths that hide their true purpose from others. Currently, making these paths is hard because methods assume too much or are specific to one problem. The authors came up with a new way using reinforcement learning, which lets agents learn to make these paths on their own. They introduced some new ideas like looking at the situation locally and representing the path-planning problem in a different way. This allows the agent’s learned behavior to work well even when it faces new situations or changes. |
Keywords
* Artificial intelligence * Generalization * Reinforcement learning
