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.
- 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:
This runs q-learning algorithm for 1500 episodes and plots the performance for every 150 episodes
$ python qlearn.py -n 1500 -p 150
The slides are adapted from the excellent lecture series by Prof. David Silver. The q-learning code is adapted from this tutorial series here.