A framework that synthesizes a tuple of optimal control policies for multi-agent systems that maximizes the probability of satisfying a desired hyperproperty.

To incorporate reward shaping approach into multi-task reinforcement learning, we propose a Centralized Reward Agent based MTRL framework (CenRA) to share and transfer knowledge across multiple tasks.