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Driving behavior modeling using naturalistic human driving data with inverse reinforcement learning
Driving behavior modeling is of great importance for designing safe, smart, and
personalized autonomous driving systems. In this paper, an internal reward function-based …
personalized autonomous driving systems. In this paper, an internal reward function-based …
Inverse reinforcement learning as the algorithmic basis for theory of mind: current methods and open problems
Theory of mind (ToM) is the psychological construct by which we model another's internal
mental states. Through ToM, we adjust our own behaviour to best suit a social context, and …
mental states. Through ToM, we adjust our own behaviour to best suit a social context, and …
Towards theoretical understanding of inverse reinforcement learning
Inverse reinforcement learning (IRL) denotes a powerful family of algorithms for recovering a
reward function justifying the behavior demonstrated by an expert agent. A well-known …
reward function justifying the behavior demonstrated by an expert agent. A well-known …
Learning multimodal rewards from rankings
Learning from human feedback has shown to be a useful approach in acquiring robot
reward functions. However, expert feedback is often assumed to be drawn from an …
reward functions. However, expert feedback is often assumed to be drawn from an …
Inverse contextual bandits: Learning how behavior evolves over time
Understanding a decision-maker's priorities by observing their behavior is critical for
transparency and accountability in decision processes {—} such as in healthcare. Though …
transparency and accountability in decision processes {—} such as in healthcare. Though …