Supervisor

Suitable for

Abstract

Reinforcement learning (RL) algorithms often require large amounts of data for training, data which is often collected from simulations of experiments of robotics systems. The requirement for large amounts of data forms a major hurdle in using RL algorithms for tasks in robotics though, where each real-world experiment would cost time and potential damage to the robot. In this project we will develop a mock "Challenge" similar to Kaggle challenges. In this challenge we will restrict the amount of data a user can query the system at each point in time, and try to implement simple RL baselines under this constraint. We will inspect the challenge definition and try to improve it.

Prerequisites: only suitable for someone who has worked in Machine Learning in the past, is familiar with Reinforcement Learning, and has strong programming skills (Python).