  Model-based reinforcement learning is a powerful tool, but collecting data to
fit an accurate model of the system can be costly. Exploring an unknown
environment in a sample-efficient manner is hence of great importance. However,
the complexity of dynamics and the computational limitations of real systems
make this task challenging. In this work, we introduce FLEX, an exploration
algorithm for nonlinear dynamics based on optimal experimental design. Our
policy maximizes the information of the next step and results in an adaptive
exploration algorithm, compatible with generic parametric learning models and
requiring minimal resources. We test our method on a number of nonlinear
environments covering different settings, including time-varying dynamics.
Keeping in mind that exploration is intended to serve an exploitation
objective, we also test our algorithm on downstream model-based classical
control tasks and compare it to other state-of-the-art model-based and
model-free approaches. The performance achieved by FLEX is competitive and its
computational cost is low.
