Project description

Reinforcement and Imitation Learning

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-qu

Tools and Technologies

• Unity3D
• Tensor-Flow Hub

Project tag

• Gaming Machine Learning
• Deep_Learning
• TensorFlow