On June 24th, 2020 from 9.00 am to 1.00 pm, the “Reinforcement Learning and application of deep-learning models in RL” seminar will take place online, within the PhD Course on Machine Learning for Non-Matrix Data, organized by profs. Giacomo Boracchi, Cesare Alippi, Matteo Matteucci.


Reinforcement learning (RL) focuses on designing agents that are able to learn how to maximize reward in unknown dynamic environments. This very general framework is motivated by a wide variety of applications ranging from recommendation systems to robotics, from treatment optimization to computer games. Unlike in other fields of machine learning, an RL agent needs to learn without a direct supervision of the best actions to take and it solely relies on the interaction with the environment and a (possibly sparse and sporadic) reward signal that implicitly defines the task to solve. Solving this problem poses several challenges such as credit assignment (understand which actions performed in the past are responsible for achieving high reward in the future), efficient exploration of the environment (to discover how the environment behaves and where most of reward is), approximation and generalization (to generalize the experience collected in some parts of the environment towards the rest of it). In the lecture, we will mostly focus on the first and last challenge. In particular, we will study how deep learning techniques can be effectively integrated into "standard" RL algorithm to be able to learn representations of the state of the environment that allow for generalization. Some of these techniques, such as DQN and TRPO, are nowadays at the core of the major successes of RL such as achieving super-human performance in games (e.g., Atari, StarCraft, Dota, and Go) as well simulated and real robotic tasks.

This event will be online and organized via Microsoft Teams.

In order to attend the seminar, please, go to the following link: Join Microsoft Teams Meeting.