Implementation of multiple collaborative agents using reinforcement learning

Abstract

The aim of this thesis is to explore the possibilities and capabilities of machine learning (ML), more specifically reinforcement learning (RL) within a 3D digital environment. By utilizing the Unity’s ML-Agents toolkit, the aim is to showcase the user-friendliness of the implementation and promote the use of the applications, emphasizing simplicity and logic. The environment takes place in Unity, where the tasks consist of training multiple agents within the same scene, to act cooperatively in order to achieve a desired behav- ior. The project consists of providing behaviour guidelines for the agents and exploring strategies to achieve better performance. An online visualization of the final product can be found in the following link. This link contains an online server where the project is hosted in the form of an interactive game. Its possible to scroll trough the different scenes of the game by pressing "1", "2", "3", "4" or "5" on your keyboard. Each scene contains a result that is relevant in this thesis. As a bonus, by pressing "5" on your keyboard, a scene where you can control one of the characters exist: https://filipbyberg.itch.io/2v2-volleyball-rl-ml-agents-showcase