  We draw on the latest advancements in the physics community to propose a
novel method for discovering the governing non-linear dynamics of physical
systems in reinforcement learning (RL). We establish that this method is
capable of discovering the underlying dynamics using significantly fewer
trajectories (as little as one rollout with $\leq 30$ time steps) than state of
the art model learning algorithms. Further, the technique learns a model that
is accurate enough to induce near-optimal policies given significantly fewer
trajectories than those required by model-free algorithms. It brings the
benefits of model-based RL without requiring a model to be developed in
advance, for systems that have physics-based dynamics.
  To establish the validity and applicability of this algorithm, we conduct
experiments on four classic control tasks. We found that an optimal policy
trained on the discovered dynamics of the underlying system can generalize
well. Further, the learned policy performs well when deployed on the actual
physical system, thus bridging the model to real system gap. We further compare
our method to state-of-the-art model-based and model-free approaches, and show
that our method requires fewer trajectories sampled on the true physical system
compared other methods. Additionally, we explored approximate dynamics models
and found that they also can perform well.
