Multi-Fidelity Reinforcement Learning for Minimum Energy Trajectory Planning

Modeling the energy consumption of a quadrotor involves complex electrical and physical dynamics, making it difficult to optimize. To address this challenge, this paper presents a multi-fidelity Gaussian process (MFGP) method that efficiently learns an accurate energy prediction model by combining many low-fidelity samples from a simple motor model with a few computationally expensive samples from a numerical battery simulation. We present extensive sample-efficiency experiments, demonstrating that a single-fidelity model often needs 10 times more high-fidelity data to match the accuracy achieved by the MFGP. The energy prediction model is then applied to a reinforcement learning (RL) agent, providing a reward signal to a minimum energy planning policy. The RL policy generates more energy efficient trajectories than those found by the minimum snap baseline method, achieving an average 3.6% energy reduction.