  We study the problem of Reinforcement Learning (RL) using as few real-world
samples as possible. A naive application of RL can be inefficient in large and
continuous state spaces. We present two versions of Multi-Fidelity
Reinforcement Learning (MFRL), model-based and model-free, that leverage
Gaussian Processes (GPs) to learn the optimal policy in a real-world
environment. In the MFRL framework, an agent uses multiple simulators of the
real environment to perform actions. With increasing fidelity in a simulator
chain, the number of samples used in successively higher simulators can be
reduced. By incorporating GPs in the MFRL framework, we empirically observe up
to $40\%$ reduction in the number of samples for model-based RL and $60\%$
reduction for the model-free version. We examine the performance of our
algorithms through simulations and through real-world experiments for
navigation with a ground robot.
