Safe Exploration in Reinforcement Learning: Theory and Applications in Robotics

2019.

Cited by: 3|Views6

Abstract:

Reinforcement learning has seen significant advances over the last decade in simulated or controlled environments. These successes have lead to interest in deploying learning algorithms in the real world, where they face significant prior uncertainties. While these algorithms are often able to find high-performance control strategies even...More

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