Flow Navigation by Smart Microswimmers via Reinforcement Learning
This work addresses navigation challenges for microswimmers in fluid environments, with potential applications in engineering adaptive micro-robots, though it is incremental in applying RL to a specific domain.
The paper tackled the problem of smart gravitactic microswimmers learning to navigate complex fluid flows to reach the highest altitude, and showed that reinforcement learning enables them to learn nearly optimal strategies, even escaping flow traps where uncontrolled swimmers fail.
Smart active particles can acquire some limited knowledge of the fluid environment from simple mechanical cues and exert a control on their preferred steering direction. Their goal is to learn the best way to navigate by exploiting the underlying flow whenever possible. As an example, we focus our attention on smart gravitactic swimmers. These are active particles whose task is to reach the highest altitude within some time horizon, given the constraints enforced by fluid mechanics. By means of numerical experiments, we show that swimmers indeed learn nearly optimal strategies just by experience. A reinforcement learning algorithm allows particles to learn effective strategies even in difficult situations when, in the absence of control, they would end up being trapped by flow structures. These strategies are highly nontrivial and cannot be easily guessed in advance. This Letter illustrates the potential of reinforcement learning algorithms to model adaptive behavior in complex flows and paves the way towards the engineering of smart microswimmers that solve difficult navigation problems.