Abstract: 

Reinforcement Learning (RL) faces the problem of learning and control in sequential decision-making problems when the environment (i.e., the dynamics and the reward) is initially unknown but can be learned through direct interaction. A fundamental step towards more sample-efficient algorithms is to devise methods to properly balance the exploration of the environment, in order to gather useful information, and the exploitation of the learned policy to collect as much reward as possible. The tutorial will provide the audience with a review of the major algorithmic principles and their integration with deep learning architectures.

Bio: 

Bio Coming Soon!