RL Intro

Material for knowledge sharing session introducing reinforcement learning.

The session has three parts:

• Introduction to RL: Slides available here. This presentation introduces the field of reinforcement learning along with the basic components.
• MDP Formulation: Slides available here. This introduces the concept of Markov Decision Process and the Q-learning algorithm.
• Q-learning Code: Code introduction for q-learning algorithm applied to the cart pole environment.

Usage Instructions

• Setup a python virtual environment of version >= 3.6
• Install the requirements for running the code:

  $ pip3 install -r requirements.txt
  

• Run the q-learning code with the default arguments:

  $ python qlearn.py -n 1500 -p 150
  

  This runs q-learning algorithm for 1500 episodes and plots the performance for every 150 episodes

Acknowledgements

The slides are adapted from the excellent lecture series by Prof. David Silver. The q-learning code is adapted from this tutorial series here.