Ongoing courses and workshops


Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision making tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more.

—— Vincent François-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare and Joelle Pineau (2018), “An Introduction to Deep Reinforcement Learning”, in Foundations and Trends in Machine Learning: Vol. 11, No. 3-4. DOI: 10.1561/2200000071. pdf

Another recent survey Deep Reinforcement Learning by Yuxi Li Under review for Morgan & Claypool: Synthesis Lectures in Artificial Intelligence and Machine Learning draw a comprehensive blueprint on modern (Deep) Reinforcement Learning.

blueprint for deep reinforcement learning

Courses and books

There are a lot of resources and courses we can refer.

Resources collection in github

and more…

Future Events

Concepts in (Deep) RL and AI

Concepts in deep reinforcement learning

Concepts in artificial intelligence


Hierarchical Reinforcement Learning

Multi-agent Reinforcement Learning and Game Theory

Sample Efficiency, Complexity and Learning Theory

Safety in Reinforcement Learning

Distributed Scalable algorithms and systems

Distributional Reinforcement Learning

Maximum Entropy RL and Probability inference

Option Discovery





Infrastructure and Computing framework