Designing a controller for autonomous vehicles capable of providing adequate performance
in all driving scenarios is challenging due to the highly complex environment and inability to …

Discovering new treatments and personalizing existing ones is one of the major goals of
modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the …

Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence
(AI) and represents a step toward building autonomous systems with a higher-level …

For sophisticated reinforcement learning (RL) systems to interact usefully with real-world
environments, we need to communicate complex goals to these systems. In this work, we …

Dexterous multi-fingered hands are extremely versatile and provide a generic way to
perform a multitude of tasks in human-centric environments. However, effectively controlling …

We propose a distributed architecture for deep reinforcement learning at scale, that enables
agents to learn effectively from orders of magnitude more data than previously possible. The …

This paper introduces SC2LE (StarCraft II Learning Environment), a reinforcement learning
environment based on the StarCraft II game. This domain poses a new grand challenge for …

Deep reinforcement learning is poised to revolutionise the field of AI and represents a step
towards building autonomous systems with a higher level understanding of the visual world …

Exploration in environments with sparse rewards has been a persistent problem in
reinforcement learning (RL). Many tasks are natural to specify with a sparse reward, and …

Conventional feedback control methods can solve various types of robot control problems
very efficiently by capturing the structure with explicit models, such as rigid body equations …