Learning Behavioral Soft Constraints from Demonstrations
This addresses the challenge of creating effective AI-human teams by equipping agents with models of human decision-making in complex environments with implicit and explicit rules, though it is incremental as it builds on prior IRL methods.
The paper tackles the problem of learning implicit human behavioral constraints from demonstrations to enable AI agents to model human trade-offs between rules and personal objectives, proposing a novel inverse reinforcement learning method that generalizes prior work and achieves state-of-the-art performance.
Many real-life scenarios require humans to make difficult trade-offs: do we always follow all the traffic rules or do we violate the speed limit in an emergency? These scenarios force us to evaluate the trade-off between collective rules and norms with our own personal objectives and desires. To create effective AI-human teams, we must equip AI agents with a model of how humans make these trade-offs in complex environments when there are implicit and explicit rules and constraints. Agent equipped with these models will be able to mirror human behavior and/or to draw human attention to situations where decision making could be improved. To this end, we propose a novel inverse reinforcement learning (IRL) method: Max Entropy Inverse Soft Constraint IRL (MESC-IRL), for learning implicit hard and soft constraints over states, actions, and state features from demonstrations in deterministic and non-deterministic environments modeled as Markov Decision Processes (MDPs). Our method enables agents implicitly learn human constraints and desires without the need for explicit modeling by the agent designer and to transfer these constraints between environments. Our novel method generalizes prior work which only considered deterministic hard constraints and achieves state of the art performance.