An application that applies the Q-learning algorithm to support an adaptation strategy for the robot in the context of an application for cognitive training. The goal is to help the user maintain a high-engaged level and stimulate in case the user is at a low-engaged level. In the project, the robot agent learns its policy by leveraging the simulator by interacting with the simulated users, updating its knowledge using the Bellman Equation. The algorithm returns a trained Q matrix(s, a). Programming Language: Python
Q-Learning algorithm for robot behaviour adaptation
Zedda E
2023
Abstract
An application that applies the Q-learning algorithm to support an adaptation strategy for the robot in the context of an application for cognitive training. The goal is to help the user maintain a high-engaged level and stimulate in case the user is at a low-engaged level. In the project, the robot agent learns its policy by leveraging the simulator by interacting with the simulated users, updating its knowledge using the Bellman Equation. The algorithm returns a trained Q matrix(s, a). Programming Language: PythonFile in questo prodotto:
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