  Using reinforcement learning to learn control policies is a challenge when
the task is complex with potentially long horizons. Ensuring adequate but safe
exploration is also crucial for controlling physical systems. In this paper, we
use temporal logic to facilitate specification and learning of complex tasks.
We combine temporal logic with control Lyapunov functions to improve
exploration. We incorporate control barrier functions to safeguard the
exploration and deployment process. We develop a flexible and learnable system
that allows users to specify task objectives and constraints in different forms
and at various levels. The framework is also able to take advantage of known
system dynamics and handle unknown environmental dynamics by integrating
model-free learning with model-based planning.
