|Machine Learning is changing the way we expect to get intelligent behaviour out of
autonomous agents. Whereas in the past the behaviour was coded by hand, it is
increasingly taught to the agent (either a robot or virtual avatar) through interaction in a
training environment. This method is used to learn behaviour for everything from industrial
robots, drones, and autonomous vehicles, to game characters and opponents. The quality
of this training environment is critical to the kinds of behaviours that can be learned, and
there are often trade-offs of one kind or another that need to be made. The typical scenario
for training agents in virtual environments is to have a single environment and agent which
are tightly coupled.
In this FYP, using Unity, we want to design a system that provide greater flexibility and ease-of-use to the growing groups interested in applying machine learning to developing
intelligent agents. Moreover, we want to do this while taking advantage of the high-quality
physics and graphics, and simple yet powerful developer control provided by the Unity
Engine and Editor. We think that this combination can benefit the following groups in ways
that other solutions might not:
- Academic researchers interested in studying complex multi-agent behaviour in
realistic competitive and cooperative scenarios.
- Industry researchers interested in large-scale parallel training regimes for robotics,
autonomous vehicle, and other industrial applications.
- Game developers interested in filling virtual worlds with intelligent agents each
acting with dynamic and engaging behaviour.