Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning
This work addresses the problem of enabling RL in real-world fluid control scenarios for researchers, though it is incremental as it focuses on a specific experimental setup.
The authors tackled the challenge of applying deep reinforcement learning to complex fluid dynamical systems by introducing 'Box o Flows', a benchtop experimental control system, and demonstrated that state-of-the-art model-free RL algorithms can synthesize complex behaviors with simple rewards.
Recent advances in real-world applications of reinforcement learning (RL) have relied on the ability to accurately simulate systems at scale. However, domains such as fluid dynamical systems exhibit complex dynamic phenomena that are hard to simulate at high integration rates, limiting the direct application of modern deep RL algorithms to often expensive or safety critical hardware. In this work, we introduce "Box o Flows", a novel benchtop experimental control system for systematically evaluating RL algorithms in dynamic real-world scenarios. We describe the key components of the Box o Flows, and through a series of experiments demonstrate how state-of-the-art model-free RL algorithms can synthesize a variety of complex behaviors via simple reward specifications. Furthermore, we explore the role of offline RL in data-efficient hypothesis testing by reusing past experiences. We believe that the insights gained from this preliminary study and the availability of systems like the Box o Flows support the way forward for developing systematic RL algorithms that can be generally applied to complex, dynamical systems. Supplementary material and videos of experiments are available at https://sites.google.com/view/box-o-flows/home.