Introduction to Reinforcement Learning

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<H2>Introduction to Reinforcement Learning with Tensorflow/Keras and OpenAI gym:<BR></H2>
<P><STRONG>Reinforcement learning (RL)</STRONG> 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 some basic Q-learning algorithms. Next, we will move into deep reinforcement learning by combining Q-learning and deep neural nets.</P>
<P>Please come with a laptop, and before the event, clone/install the repo <A HREF="https://github.com/kengz/openai_gym" TARGET="_blank" TITLE="https://github.com/kengz/openai_gym" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer"><SPAN>https://github.com/kengz/openai_gym</SPAN></A></P>
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<H2>Speakers:</H2>
<P><STRONG>Wah Loon Keng (<A HREF="http://www.twitter.com/kengzwl" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">@kengzwl</A>)</STRONG> is a software engineer at <A HREF="http://www.eligible.com" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Eligible API</A>. 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 <A HREF="http://www.tflearn.org" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">TFLearn</A> and <A HREF="http://www.spacy.io" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">spaCy</A>.</P>
<P><STRONG>Laura Graesser (<A HREF="http://www.twitter.com/lgraesser3" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">@lgraesser3</A>)</STRONG> 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.</P>
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<H2>Agenda:</H2>
<P>1pm - Doors open. You will need to sign-in with our Envoy visitor system. Free time to get situated. There will be free pizza & drinks.</P>
<P>2-5pm - Keng & Laura will lead the talk.</P>

42 West St.

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Introduction to Reinforcement Learning

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Introduction to Reinforcement Learning

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