Vinyals M. auteur aut IRD Sabbadin R. auteur aut IRD Couture S. auteur aut IRD Sadou L. auteur aut IRD Thomopoulos R. auteur aut IRD Chapuis Kevin auteur aut IRD Lesquoy Baptiste auteur aut IRD Taillandier Patrick auteur aut IRD text journalArticle eng text/pdf reformatted digital access In this paper, we tackle innovation diffusion from the perspective of an institution which aims to encourage the adoption of a new product (i.e., an innovation) with mostly social rather than individual benefits. Designing such innovation adoption policies is a very challenging task because of the difficulty to quantify and predict its effect on the behaviors of non-adopters and the exponential size of the space of possible policies. To solve these issues, we propose an approach that uses agent-based modeling to simulate in a credible way the behaviors of possible adopters and (deep) reinforcement learning to efficiently explore the policy search space. An application of our approach is presented for the question of the use of digital technologies in agriculture. Empirical results on this case study validate our scheme and show the potential of our approach to learn effective innovation diffusion policies. specialized innovation diffusion policy design reinforcement learning agent-based simulation deep reinforcement learning digital agriculture 020 076 9 1000785 [17 ] 2023 https://www.documentation.ird.fr/hor/fdi:010087623 10.3389/fams.2023.1000785 [F B010087623] https://www.documentation.ird.fr/hor/fdi:010087623 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2023-06/010087623.pdf IRD - Base Horizon / Pleins textes 2023-06-30 2025-02-24 fdi:010087623 fre