Reinforcement learning (RL) provides a unique framework for addressing sequential
decision-making problems. Despite the numerous software frameworks proposed to
accelerate the development of new algorithms and applications, RL researchers and
practitioners often still rely on custom code. This thesis identifies and addresses
some core issues contributing to this trend. In the first part, we propose a modular
approach for defining distributed RL schemes using basic, reusable building blocks.
In the second part, we contribute to the creation of TorchRL, the official PyTorch
domain library for general decision-making. TorchRL is designed to be efficient,
scalable, and broadly applicable. Finally, we leverage and validate TorchRL by
developing ACEGEN, a library for language-based generative drug discovery, and
use it to explore new solutions in this field.
(Universitat Pompeu Fabra, 2025-04-11T09:23:14Z) Bou Hernández, Albert; De Fabritiis, Gianni; Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions
