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Learning Behavioral Soft Constraints from Demonstrations
Arie Glazier · Andrea Loreggia · Nicholas Mattei · Taher Rahgooy · Francesca Rossi · Brent Venable

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 norms and our own personal objectives. To this end, we propose a novel inverse reinforcement learning (IRL) method for learning implicit hard and soft constraints from demonstrations, enabling agents to quickly adapt to new settings. In addition, learning soft constraints over states, actions, and state features allows agents to transfer this knowledge to new domains that share similar aspects.

Author Information

Arie Glazier (Tulane University)
Andrea Loreggia (University of Bologna)
Nicholas Mattei (Tulane University)
Taher Rahgooy (University of West Florida)
Francesca Rossi (IBM)
Brent Venable (Institute for Human and Machine Cognition)

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