Deep Reinforcement Learning has been becoming very popular since the dawn of DeepMind's AlphaGo and DQN. Algorithms that learn to solve a game (sometimes better than) humans seems very complex from a distance, and we shall unravel the mathematical workings of such models through simple processes. This workshops aims to provide a simple insight about Reinforcement Learning and going to Deep RL.
This session aims to give a gentle introduction to Reinforcement Learning for beginners and moving from simple Dynamic Programming based approaches to Deep RL methods leveraging a variety of Deep Learning methods including but not limited to Convolutions, Recurrent architectures, Attention mechanisms etc. All demonstrations will make use of Keras (with Tensorflow backend) running on a Jupyter Notebook. Some advanced experiments involve the use of OpenAI gym (library for simulating game environments) and/or PyGame. The workshop will be divided into the following sections:
Introduction (20 mins)
Q-Learning (35 mins)
Deep Q-learning (35 mins)
Final Project (~20 mins)
After this workshop, you should be able to build your own reinforcement models for solving semi-supervised tasks and catch-up with the recent research in Deep Reinforcement Learning. A concluding discussion about how to progress further and doubts.