How to bring reinforcement learning to the real world

Labor- and data-efficiency remain two of the key challenges of artificial intelligence. In recent decades, researchers have proven that big data and machine learning algorithms reduce the need for providing AI systems with prior rules and knowledge. But machine learning—and more recently deep learning—have presented their own challenges, which require manual labor albeit of different nature.

Creating AI systems that can genuinely learn on their own with minimal human guidance remain a holy grail and a great challenge. According to Sergey Levine, assistant professor at the University of California, Berkeley, a promising direction of research for the AI community is “self-supervised offline reinforcement learning.”

This is a variation of the RL paradigm that is very close to how humans and animals learn to reuse previously acquired data and skills, and it can be a great boon for applying AI to real-world settings. In a paper titled “Understanding the World Through Action” and a talk at the NeurIPS 2021 conference, Levine explained how self-supervised learning objectives and offline RL can help create generalized AI systems that can be applied to various tasks.

Read the full article on TechTalks.

For more on reinforcement learning research:

• DeepMind RL method promises better co-op between AI and humans

• Reinforcement learning frustrates humans in teamplay, MIT study finds

• Stanford reinforcement learning system simulates evolution

• Stanford reinforcement learning system simulates evolution