Fundamentals of Reinforcement Learning (12 hours)
Teacher: Vieri G. Santucci (CNR)
The course aims at providing an introduction to reinforcement learning (RL): from a brief introduction on its relations with biological processes, to its implementation within the machine learning framework. The typologies of problems tackled with RL will be discussed and presented both with simple examples and with state-of-the art works where RL is applied to autonomous open-ended learning in robots.
- Santucci, V. G., Baldassarre, G., Mirolli, M. (2016) GRAIL: A goal-discovering robotic architecture for intrinsically-motivated learning. IEEE Transactions on Cognitive and Developmental Systems, 8(3), 214-231.
- Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275(5306), 1593-1599.
- Sutton, R. S., Barto, A. G. (1998/2018) Reinforcement Learning: An Introduction. Cambridge: MIT press.