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Reinforcement learning can do some pretty impressive things. It can optimize ad targeting, help run self-driving cars, and even win StarCraft games. But current RL systems are still highly task-specific. Tesla\\u2019s self-driving car algorithm can\\u2019t win at StarCraft, and DeepMind\\u2019s AlphaZero algorithm can with Go matches against grandmasters, but can\\u2019t optimize your company\\u2019s ad spend.

So how do we make the leap from narrow AI systems that leverage reinforcement learning to solve specific problems, to more general systems that can orient themselves in the world? Enter Tim Rockt\\xe4schel, a Research Scientist at Facebook AI Research London and a Lecturer in the Department of Computer Science at University College London. Much of Tim\\u2019s work has been focused on ways to make RL agents learn with relatively little data, using strategies known as sample efficient learning, in the hopes of improving their ability to solve more general problems. Tim joined me for this episode of the podcast.