Abstract:

Since the advent of deep learning progress in the field has been driven by two key factors: the availability of large amount of data and the ability to train large parametric models using this data. In this talk, we will explore how these core principles have been actualized in model-free reinforcement learning through the integration of parallel training frameworks and GPU-accelerated simulators, enabling unprecedented results in various domains.