Physics-Informed Model and Hybrid Planning for Efficient Dyna-Style Reinforcement Learning
This addresses efficiency challenges in RL for real-world applications, but appears incremental as it builds on existing Dyna-style and hybrid planning approaches.
The paper tackled the trade-off between asymptotic performance, sample efficiency, and inference time in reinforcement learning by leveraging partial physical knowledge, resulting in improved compromise over state-of-the-art methods.
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.