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Learning-from-demonstrations is an emerging paradigm to obtain effectiverobot control policies for complex tasks via reinforcement learning without theneed to explicitly design reward functions. However, it is susceptible toimperfections in demonstrations and also raises concerns of safety andinterpretability in the learned control policies. To address these issues, weuse Signal Temporal Logic to evaluate and rank the quality of demonstrations.Temporal logic-based specifications allow us to create non-Markovian rewards,and also define interesting causal dependencies between tasks such assequential task specifications. We validate our approach through experiments ondiscrete-world and OpenAI Gym environments, and show that our approachoutperforms the state-of-the-art Maximum Causal Entropy Inverse ReinforcementLearning.

Learning from Demonstrations using Signal Temporal Logic