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

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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Bibliographic Details
Main Authors: Francisco Martinez-Gil, Miguel Lozano, Ignacio García-Fernández, Pau Romero, Dolors Serra, Rafael Sebastián
Format: Article
Language:English
Published: MDPI AG 2020-09-01
Series:Mathematics
Subjects:
inverse reinforcement learning
optimization
causal entropy
reinforcement learning
learning by demonstration
pedestrian simulation
Online Access:https://www.mdpi.com/2227-7390/8/9/1479

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