Transfer in Reinforcement Learning Domains

• Κύριος συγγραφέας: Taylor, Matthew E. (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2009.
• Σειρά: Studies in Computational Intelligence, 216
• Θέματα: Engineering.; Artificial intelligence.; Computational intelligence.; Computational Intelligence.; Artificial Intelligence (incl. Robotics).

In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. The RL framework has gained popularity with the development of algorithms capable of mastering increasingly complex problems, but learning difficult tasks is often slow...

Πίνακας περιεχομένων:

• Reinforcement Learning Background
• Related Work
• Empirical Domains
• Value Function Transfer via Inter-Task Mappings
• Extending Transfer via Inter-Task Mappings
• Transfer between Different Reinforcement Learning Methods
• Learning Inter-Task Mappings
• Conclusion and Future Work.