Highly stochastic analytic meta-learning: the Braitenberg vehicles case study

The goal of this project was to investigate meta-learning in reinforcement learning under highly stochastic environments using mathematically tractable and autonomous Braitenberg vehicles.


  Project Funders

Royal Society

  Funding Amount

£9,440

About the Project

The goal of this project was to investigate meta-learning in reinforcement learning under highly stochastic environments using mathematically tractable and autonomous Braitenberg vehicles. In collaboration with Dr. Mehdi Khamassi (Institut des Systemes Intelligents et de Robotique, CNRS-Universite Pierre et Marie Curie, Paris, France).


  People

Researchers

Dr. Inaki Rano