We will kick off this meetup with a quick introduction to some RL definitions as outlined in the blog post by Tambet Matiisen: “Demystifying Deep Reinforcement Learning”. Then Daniel Slater will introduce Q-learning based on his blog and his presentation at PyData.
Recently, researchers at DeepMind have pioneered a novel, parallel approach called asynchronous reinforcement learning. When applied to a variety of Atari 2600 domains, asynchronous reinforcement learning achieves better results while also requiring much less computational resources compared to previous deep reinforcement learning algorithms.
In October's Journal Club:
1.) We will kick off with a quick introduction to some RL definitions as outlined in the blog post by Tambet Matiisen: “Demystifying Deep Reinforcement Learning”.
2.) We are very happy to have Daniel Slater along to introduce the concepts behind his AlphaToe project, which seeks to apply the techniques used in DeepMind's AlphaGo to Tic-Tac-Toe. This is written in Python using TensorFlow and the Github is here.
3.) We will take a look at Corey Lynch’s implementation of asynchronous 1-step Q learning in Tensorflow + Keras + OpenAI’s Gym framework. This algorithm was published in “Asynchronous Methods for Deep Reinforcement Learning” by Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver and Koray Kavukcuoglu.
• David Silver's tutorial on Deep Reinforcement Learning
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