Saturday 2:30 PM–4:30 PM in C02

Deep Reinforcement Learning: A hands-on approach

Shubham Dokania

Audience level:


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:

  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.

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