  Our objective of this project is to make the extension based on the technique
mentioned in the paper "Safety-Aware Apprenticeship Learning" to improve the
utility and the efficiency of the existing Reinforcement Learning model from a
Single-Agent Learning framework to a Multi-Agent Learning framework. Our
contributions to the project are presented in the following bullet points: 1.
Regarding the fact that we will add an extension to the Inverse Reinforcement
Learning model from a Single-Agent scenario to a Multi-Agentscenario. Our first
contribution to this project is considering the case of extracting safe reward
functions from expert behaviors in a Multi-Agent scenario instead of being from
the Single-Agent scenario. 2. Our second contribution is extending the
Single-Agent Learning Framework to a Multi-Agent Learning framework and
designing a novel Learning Framework based on the extension in the end. 3. Our
final contribution to this project is evaluating empirically the performance of
my extension to the Single-Agent Inverse Reinforcement Learning framework.

Artificial Intelligence;Machine Learning