Automata Guided Reinforcement Learning With Demonstrations

Understand

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.