Using inverse reinforcement learning with real trajectories to get more trustworthy pedestrian simulations

HIGHLIGHTS

• who: Francisco Martinez-Gil et al. from the address:, Valencia, Spain have published the article: Using Inverse Reinforcement Learning with Real Trajectories to Get More Trustworthy Pedestrian Simulations, in the Journal: (JOURNAL)
• what: The authors propose the use of Inverse Reinforcement Learning to incorporate real behavior traces in the learning process to shape the learned behaviors thus increasing their trustworthiness (in terms of conformance to reality). The authors focus on the problem of simulating the navigation of a pedestrian 3 of 15 inside a 3D environment representing a simple maze. It is assumed that the . . .
  

   

  

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