CORDIS Project
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This project focuses on developing a scalable methodology for reinforcement learning in complex, dynamic environments. It aims to enhance learning efficiency by identifying key features, optimizing skills, and adjusting reward structures, with applications in flight simulation and smart grid control.
Learning how to act optimally in high-dimensional stochastic dynamic environments is a fundamental problem in many areas of engineering and computer science.
The basic setup is that of an agent who interacts with an environment trying to maximize some long term payoff while having access to observations of the state of the environment. A standard approach to solving this problem is the Reinforcement Learning (RL) paradigm in which an agent is trying to improve its policy by interacting with the…
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