Introduction, without any prior requirement except being able to code, to the wide world of Reinforcement Learning. This course teaches how to make artificial agents that learn by “trial and error”, suited for various kinds of simple to complicated tasks. It also covers the basics of Neural Networks, Deep Learning, Control Engineering, Stochastic Optimization and Planning.
The OpenAI Gym will be use as a testbed for learning algorithms (if you do not program your own environment, which is also allowed).
Experiments will be coded in Python. We recommend the use of “pip”, available on any distribution, and with every Python library available with a single “pip3 install library“. For Windows users, pip is also available with recent versions of Python 3, but in case of any problem, here is a link to the Anaconda framework.