- Chapter: Lampton, Amanda, and <b>Valasek, John</b>, “Multi-Resolution State-Space Discretization Method for Q-Learning for One or More Regions of Interest”
From AIAA:
“Research advances in embedded computational intelligence, communication, control, and new mechanisms for sensing, actuation, and adaptation hold the promise to transform aerospace. The result will be air and space vehicles, propulsion systems, exploration systems, and vehicle management systems that respond more quickly, provide large-scale distributed coordination, work in dangerous or inaccessible environments, and augment human capabilities.
Advances in Intelligent and Autonomous Aerospace Systems seeks to provide both the aerospace researcher and the practicing aerospace engineer with an exposition on the latest innovative methods and approaches that focus on intelligent and autonomous aerospace systems.
The chapters are written by leading researchers in this field, and include ideas, directions, and recent results on current intelligent aerospace research issues with a focus on dynamics and control, systems engineering, and aerospace design. The content on uncertainties, modeling of large and highly non-linear complex systems, robustness, and adaptivity is intended to be useful in both the subsystem and the overall system-level design and analysis of various aerospace vehicles. A broad spectrum of methods and approaches are presented, including:
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Bio-inspiration
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Fuzzy logic
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Genetic algorithms
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Q-learning
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Markov decision processes
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Approximate dynamic programming
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Artificial neural networks
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Probabilistic maps
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Multi-agent systems
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Kalman, particle, and confidence filtering”
