Automata Guided Reinforcement Learning With Demonstrations
  Tasks with complex temporal structures and long horizons pose a challenge for
reinforcement learning agents due to the difficulty in specifying the tasks in
terms of reward functions as well as large variances in the learning signals.
We propose to address these problems by combining temporal logic (TL) with
reinforcement learning from demonstrations. Our method automatically generates
intrinsic rewards that align with the overall task goal given a TL task
specification. The policy resulting from our framework has an interpretable and
hierarchical structure. We validate the proposed method experimentally on a set
of robotic manipulation tasks.
