Data Science in Practice: Reinforcement Learning with Tensorflow and OpenAI Gym

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Reinforcement learning (RL) is the technique used by DeepMind to play the Atari games and Go. We will learn about the basic theory of RL, explore the playground of the OpenAI Gym, and play some games using basic Q-learning algorithms. Next, we will move into deep reinforcement learning by combining Q-learning and deep neural nets.

Please come with a laptop. Before the event, clone/install the repo:

https://github.com/kengz/openai_gym

Although we've listed 1:45 pm as the start time, doors open at 1:00pm for people who want to socialize over pizza, snacks, and drinks. The session starts at 2:00 pm.

This is a free event. In order to allow us to track RSVPs and manage limited space, please register here as well: Reinforcement Learning with Tensorflow and OpenAI (https://www.eventbrite.com/e/introduction-to-reinforcement-learning-tickets-28774248552)

Speakers:

Wah Loon Keng is a software engineer at Eligible API. He's a mathematician at heart, who specializes in graphs, complexity, computation, and their roles in artificial intelligence and computational cognitive science. Keng also actively contributes to open source projects such as TFLearn and spaCy.

Laura Graesser is studying for an MS in computer science at NYU, focusing on machine learning. Laura is particularly interested in neural networks and their application to computer vision problems, cross-fertilization between computer vision and NLP, the representations perspective (machine learning as data transformation and representation), and the manifold hypothesis.

This event is hosted by Eligible API. Eligible is a Brooklyn-based technology company, focused on providing APIs for medical eligibility, claims, and payments.