EmpiricalRL

Repo for UofA Empirical RL Grad class Winter 2021

Prerequisites: An undergraduate or graduate level course on Reinforcement Learning (e.g., CMPUT 397, CMPUT 366 from 2018), or successfully complete the 4-course UofA RL MOOC.

This is an advanced class on Reinforcement Learning. We will not cover the basics of RL; it will be assumed students already know the material.

Description: This course will focus on doing good experiments in reinforcement learning (RL). Reinforcement Learning is a fast growing field. Learning systems are becoming more complex and are routinely applied to complex games, 3D simulators, and robots. It is challenging to evaluate these systems because performance depends on carefully setting numerous hyper-parameters and each experiment may consume vast amounts of data and compute—sometimes running for days over even weeks on super clusters. It is not secret that many of the empirical results published in the RL literature are suspect or flat out misleading. This course will focus on designing and conducting good experiments in RL. We will survey best practices and criticism of popular methodologies used in the field. The objective of the course is to train each student to be a good RL empiricist—which will be demonstrated with a final project focused on conducting a good experiment. The class will be a mix of lecture, student presentations on papers from the literature, and the final project.