14 August 2020
Deep Reinforcement Learning for Computer Games
Due to the covid-19 outbreak, all programmes for 2020 have been cancelled.
Basics of machine learning (one day + practicals)
The basics of supervised and unsupervised learning. Supervised learning portion will first cover the task definition and classical approaches. But we will quickly move to modern deep learning -based models and algorithms. We will cover following models: classical feedforward, convolutive and recurrent networks. In addition we will cover generative adversarial networks (GANs) and variational autoencoders (VAE). This section will also include hands on portion.
Autonomous agents (two days lectures + practicals)
At first reinforcement learning will be introduced and the basic techniques will be reviewed. We wil then move to deep reinforcement learning (DRL), where RL can be easily applied to visual input tasks. The focus in the course is how to teach autonomous agents to play computer games, so we will show and use in practice some successful DRL learning environments, such as VizDoom, Unity OTC and so on. This section will also include hands on portion.
Autonomous agents project work (two days)
During this section students will develop (using Python) and train an autonomous agent that will be able to play a computer game. We will offer an learning environment for Toribash fighting game so that students will be able to easily start with developing their own agents. Agents then will be playing against each other and human players. Students will be offered a realtime LeaderBoard, where they can follow the progress of their agent (how well it plays against the other agents).
Ville Hautamäki, villeh(at)cs.uef.fi
Basics of machine learning, autonomous agents (like robots)
ECTS (+ project work 2 ECTS)
EUR 300: Course fee. There is a discount of 20 % for the UEF partner university students.
EUR 200: Course fee for exchange students starting in autumn 2020.