Using Inverse Reinforcement Learning with Real Trajectories to Get More Trustworthy Pedestrian Simulations

Reinforcement learning is one of the most promising machine learning techniques to get intelligent behaviors for embodied agents in simulations. The output of the classic Temporal Difference family of Reinforcement Learning algorithms adopts the form of a value function expressed as a numeric table...

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Bibliografiska uppgifter

Huvudupphov:	Francisco Martinez-Gil, Miguel Lozano, Ignacio García-Fernández, Pau Romero, Dolors Serra, Rafael Sebastián
Materialtyp:	Artigo
Språk:	Inglês
Utgiven:	MDPI AG 2020-09-01
Serie:	Mathematics
Ämnen:	inverse reinforcement learning optimization causal entropy reinforcement learning learning by demonstration pedestrian simulation
Länkar:	https://www.mdpi.com/2227-7390/8/9/1479
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