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:

1. Introduction (20 mins)

   • Introduction to Reinforcement Learning and problem description.
   • Intuition about observation-reward based learning and policy evaluation.
   • Markov Decision Process (MDP)

2. Q-Learning (35 mins)

   • Discussion about Value learning and Q-learning.
   • Understanding Q-learning with a grid world (Toy problem)
   • Learning about playing games from visual input.

3. Deep Q-learning (35 mins)

   • Improvement over simple Q-learning (Approximate Q-learning)
   • SARSA with Deep learning, A simple example on a toy game (Tic-Tac-Toe or Pong)
   • DQN for Atari games, a brief discussion

4. Final Project (~20 mins)

   • Implement the above algorithms on a few games.
   • Games including Flappy bird, 2048 etc.
   • Discussion about what to do next and how the field is progressing.

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.