Real-World Reinforcement Learning via Multifidelity Simulators

Robotics, IEEE Transactions, Volume 31, Issue 3, 2015, Pages 655-671.

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Abstract:

Reinforcement learning (RL) can be a tool for designing policies and controllers for robotic systems. However, the cost of real-world samples remains prohibitive as many RL algorithms require a large number of samples before learning useful policies. Simulators are one way to decrease the number of required real-world samples, but imperfe...More

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