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  • Ongoing Project by Robin Schiewer: Efficient Exploration Strategies in Reinforcement Learning
Ongoing Project by Robin Schiewer: Efficient Exploration Strategies in Reinforcement Learning

One of the main problems in reinforcement learning is to achieve sufficient exploration of the agent's environment. If there is no knowledge about all the reward that is to gather, an optimal decision can hardly be made. However, not leveraging what has been learned so far may slow down the learning progress or make learning unstable.

There exist many exploration strategies to deal with this exploration-exploitation-tradeoff, but they each come with their own strengths and weakensses. The idea of this project is to investigate concepts from model-based reinforcement learning to achieve robust, situation-aware exploration of static and non-static environments.

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

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