Deep Reinforcement Learning
Deep Reinforcement Learning


Reinforcement Learning recently progressed greatly in industry as one of the best techniques for sequential decision making and control policies.

DeepMind used RL to greatly reduce energy consumption in Google's data centre. It has being used to do text summarisation, autonomous driving, dialog systems, media advertisements and in finance by JPMorgan Chase. We are at the very beginning of the adoption of these algorithms as systems are required to operate more and more autonomously.
In this workshop we will explore Reinforcement Learning, starting from its fundamentals and ending creating our own algorithms.

We will use OpenAI gym to try our RL algorithms. OpenAI is a non profit organisation that want committed to open source all their research on Artificial Intelligence. To foster innovation OpenAI created a virtual environment, OpenAi gym, where it's easy to test Reinforcement Learning algorithms.

In particular we will start with some popular techniques like Multi Armed Bandit, going thought Markov Decision Processes and Dynamic Programming.

We then will also explore other RL frameworks and more complex concepts like Policy gradients methods and Deep Reinforcement learning, which recently changed the field of Reinforcement Learning. In particular we will see Actor Critic models and Proximal Policy Optimizations that allowed OpenAI to beat some of the best Dota players.

We will also provide the necessary Deep Learning concepts for the course.


Leonardo De Marchi holds a Master in Artificial intelligence and has worked as a Data Scientist in the sport world, with clients such as the New York Knicks and Manchester United, and with large social networks, like Justgiving.

He now works as Head of Data Scientist and Analytics in Badoo, the largest dating site with over 420 million users. He is also the lead instructor at, a company specialized in Reinforcement Learning, Deep Learning and Machine Learning training.

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