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Learning Behavioral Soft Constraints from Demonstrations
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?
Making Human-Like Trade-offs in Constrained Environments by Learning from Demonstrations
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.
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