  We present a novel approach (DyNODE) that captures the underlying dynamics of
a system by incorporating control in a neural ordinary differential equation
framework. We conduct a systematic evaluation and comparison of our method and
standard neural network architectures for dynamics modeling. Our results
indicate that a simple DyNODE architecture when combined with an actor-critic
reinforcement learning (RL) algorithm that uses model predictions to improve
the critic's target values, outperforms canonical neural networks, both in
sample efficiency and predictive performance across a diverse range of
continuous tasks that are frequently used to benchmark RL algorithms. This
approach provides a new avenue for the development of models that are more
suited to learn the evolution of dynamical systems, particularly useful in the
context of model-based reinforcement learning. To assist related work, we have
made code available at https://github.com/vmartinezalvarez/DyNODE .
