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Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processes
Safety-critical technical systems operating in unknown environments require the ability to quickly adapt their behavior, which can be achieved in control by inferring a model online from the data stream generated during operation.
Personalized Rehabilitation Robotics based on Online Learning Control
The use of rehabilitation robotics in clinical applications gains increasing importance, due to therapeutic benefits and the ability to alleviate labor-intensive works.
Inverse Reinforcement Learning: A Control Lyapunov Approach
A common approach to solve this problem is the framework of inverse reinforcement learning (IRL), where the observed agent, e. g., a human demonstrator, is assumed to behave according to an intrinsic cost function that reflects its intent and informs its control actions.
Deep Decentralized Reinforcement Learning for Cooperative Control
In order to collaborate efficiently with unknown partners in cooperative control settings, adaptation of the partners based on online experience is required.
Multi-agent Reinforcement Learning
reinforcement-learning
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