The prestigious Turing Award, known as the ‘Nobel Prize in Computing’, was awarded to the pioneers of reinforcement learning, Andrew G. Barto and Richard S. Sutton. This discipline has revolutionised the field of artificial intelligence by enabling machines to learn from experience and serves as the basis for numerous innovative projects, including PILLAR-Robots.

PILLAR is an advanced research initiative that uses reinforcement learning to develop autonomous systems that can dynamically adapt to complex scenarios.

Following the announcement of the Turing Award, Vieri Giuliano Santucci, principal investigator at CNR, was asked to comment on the significance of this award in la Repubblica, one of Italy’s leading newspapers. In his statement, he emphasised how reinforcement learning has paved the way for machines to interact with dynamic and unfamiliar environments.

According to Vieri Giuliano Santucci, these theories significantly impact robotics: “We see them in action in artificial agents that must interact with the real world: it is not possible to pre-program a complete learning dataset in advance. Reinforcement learning, which mirrors the learning mechanisms of living beings, instead opens the doors of machine learning to interaction with dynamic and potentially unknown environments,” the researcher concluded.

The Turing Award to the pioneers of reinforcement learning is not just an individual award but a tribute to the entire field of artificial intelligence and its extraordinary potential. This technology is already transforming numerous application areas, from robotics to automation, making machines increasingly capable of learning from experience and adapting to dynamic contexts.

The PILLAR project is a concrete example of this, showing how it contributes significantly to the evolution of these technologies. Looking forward, reinforcement learning will continue to play a central role in the development of increasingly intelligent and autonomous systems, with a profound impact on our society.