# Multi-Fidelity Reinforcement Learning with Gaussian Processes

arXiv: https://arxiv.org/abs/1712.06489 · HTML: https://mlsanity.com/paper/1712.06489/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2017.

## Built on by (most cited first, 2 of 2)

- [A Multi-Fidelity Control Variate Approach for Policy Gradient Estimation](https://mlsanity.com/paper/2503.05696/) (2025)
- [Multi-fidelity Reinforcement Learning Control for Complex Dynamical Systems](https://mlsanity.com/paper/2504.05588/) (2025)

## Abstract (arXiv)

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.