Reinforcement learning with multi-fidelity simulators

Robotics and Automation, 2014, Pages 3888-3895.

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Abstract:

We present a framework for reinforcement learning (RL) in a scenario where multiple simulators are available with decreasing amounts of fidelity to the real-world learning scenario. Our framework is designed to limit the number of samples used in each successively higher-fidelity/cost simulator by allowing the agent to choose to run traje...More

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