Published January 30, 2023 | Version v1
Dataset Open

Policies used in "Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark"

Authors/Creators

  • 1. Aix Marseille Univ, CNRS, Centrale Marseille, IRPHE, Marseille, France
  • 2. Dept. Physics and INFN, University of Rome ``Tor Vergata'', Via della Ricerca Scientifica 1, 00133 Rome, Italy

Description

Policies used in the paper "Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark". They can be tested using the software "OTTO-benchmark" available at https://github.com/auroreloisy/otto-benchmark.

Files

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Additional details

Funding

European Commission
Smart-TURB - A Physics-Informed Machine-Learning Platform for Smart Lagrangian Harness and Control of TURBulence 882340
European Commission
C0PEP0D - Life and death of a virtual copepod in turbulence 834238