Curriculum learning is often employed in deep reinforcement learning to let the agent progress more quickly towards better behaviors. Numerical methods for curriculum learning in the literature provides only initial heuristic solutions, with little to no guarantee on their quality. We define a new gray-box function that, including a suitable scheduling problem, can be effectively used to reformulate the curriculum learning problem. We propose different efficient numerical methods to address this gray-box reformulation. Preliminary numerical results on a bench- mark task in the curriculum learning literature show the viability of the proposed approach.
2019, Optimization of Complex Systems: Theory, Models, Algorithms and Applications, Pages 720-729
A Gray-Box Approach for Curriculum Learning (02a Capitolo o Articolo)
Foglino Francesco, Leonetti Matteo, Sagratella Simone, Seccia Ruggiero
ISBN: 978-3-030-21802-7; 978-3-030-21803-4
Gruppo di ricerca: Continuous Optimization