Learning Logic Constraints from Demonstration

Abstract

Autonomous agents operating in real-world settings are often required to efficiently accomplish a task while adhering to certain environmental constraints. For instance, a self-driving car must transport its passengers to their intended destination as fast as possible while complying with traffic regulations. Inverse Constrained Reinforcement Learning (ICRL) is a technique that enables the learning of a policy from demonstrations of expert agents. When these expert agents adhere to the environmental constraints, ICRL thus allows for compliant policies to be learned without the need to define constraints beforehand. However, this approach provides no insight into the constraints themselves although this is desired for safety-critical applications such as autonomous driving. In such settings, it is important to verify what is learned from the given demonstrations. In this work, we propose a novel approach for learning logic rules that represent the environmental constraints given demonstrations of agents that comply with them, thus providing an interpretable representation of the environmental constraints.