Мини-курс научного руководителя HDI Lab, профессора Эрика Мулина "Introduction to reinforcement learning"

Annotation: Reinforcement learning is learning to maximize a numerical reward signal. The learner is not told which actions to take, but instead must discover which actions yield the most reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward but also the next situation and, through that, all subsequent rewards. These two characteristics — trial-and-error search and delayed reward — are the two most important distinguishing features of reinforcement learning. 

The minicourse is an elementary introduction of reinforcement learning. We will formalize the problem of reinforcement learning using ideas from dynamical systems theory, specifically, as the optimal control of incompletely-known Markov decision processes. A learning agent must be able to sense the state of its environment to some extent and must be able to take actions that affect the state. The agent also must have a goal or goals relating to the state of the environment. Markov decision processes are intended to include just these three aspects—sensation, action, and goal—in their simplest possible forms without trivializing any of them. Any method that is well suited to solving such problems we consider to be a reinforcement learning method. The course is an elementary introduction covering:

• An introduction to reinforcement learning
• Markov decision processes
• Dynamic programming
• Monte Carlo methods
• Temporal difference learning

We will follow closely the book “Reinforcement Learning: an introduction”, Barto Sutton, MIT Press, and the use the pedagogical material proposed by David Silver.

If you need a pass to the building, please contact Vlada Kuznetsova at vkuznecova@hse.ru