  Applying reinforcement learning (RL) to real-world applications requires
addressing a trade-off between asymptotic performance, sample efficiency, and
inference time. In this work, we demonstrate how to address this triple
challenge by leveraging partial physical knowledge about the system dynamics.
Our approach involves learning a physics-informed model to boost sample
efficiency and generating imaginary trajectories from this model to learn a
model-free policy and Q-function. Furthermore, we propose a hybrid planning
strategy, combining the learned policy and Q-function with the learned model to
enhance time efficiency in planning. Through practical demonstrations, we
illustrate that our method improves the compromise between sample efficiency,
time efficiency, and performance over state-of-the-art methods.
