ξ-Learning: Successor Feature Transfer Learning for General Reward Functions

by Chris Reinke and Xavier Alameda-Pineda [Paper]                 [Code] Abstract. Transfer in Reinforcement Learning aims to improve learning performance on target tasks using knowledge from experienced source tasks. Successor features (SF) are a prominent transfer mechanism in domains where the reward function changes between…

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[Closed] Master Internship on Meta and Transfer Learning for Deep Reinforcement Learning in Robotics

Topic: The internship is part of our research project into Deep Reinforcement Learning (DRL) [1] for the European SPRING project. SPRING aims to develop control mechanisms for mobile robots that will be employed in hospitals and health care environments. The robots should communicate with elderly people, their families and caretakers…

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