Reinforcement Learning is learning what to do – what action to take in a specific situation – in order to maximize some type of reward. It’s one of the most promising areas of Machine Learning today. It plays an important part in some very high-profile success stories of AI, such as mastering Go, learning to play computer games, autonomous driving, autonomous stock trading, and more. Expect an introduction the main theoretical and practical aspects of Reinforcement Learning, discuss its very distinctive set of challenges, and explore what the future looks like for self-training machines.
AI Progress Panel benefited from the knowledge sharing from the fields of Deep Learning for RecSys, NLP, ML, ML Models for NLP, time series analysis and a lot more.
Speakers: Alexandra Petruș (Opening), Raoul Adalbert (Reinforcement Learning), Sorin Pește (An Overview of Reinforcement Learning), Andrei Vlacu (A real-life application: Using Tensorflow in the esports industry). Panel: Sergii Khomenko, Krzysztof Suwada, Oleksandr Zakharchuk, Christian Merkwirth – AI Progress, moderated by Sorin Pește.
Below, photos from the event, some slides and a video recording of the event.