Reinforcement learning for pursuit and evasion of microswimmers at low Reynolds number
This work addresses a domain-specific problem in microswimmer control, offering incremental improvements in strategy development for such environments.
The study tackled the problem of pursuit and evasion between microswimming agents in a low-Reynolds-number environment with partial observability, using adversarial reinforcement learning to train agents that outperformed heuristic strategies by discovering complex sequences of moves.
We consider a model of two competing microswimming agents engaged in a pursue-evasion task within a low-Reynolds-number environment. Agents can only perform simple maneuvers and sense hydrodynamic disturbances, which provide ambiguous (partial) information about the opponent's position and motion. We frame the problem as a zero-sum game: The pursuer has to capture the evader in the shortest time, while the evader aims at deferring capture as long as possible. We show that the agents, trained via adversarial reinforcement learning, are able to overcome partial observability by discovering increasingly complex sequences of moves and countermoves that outperform known heuristic strategies and exploit the hydrodynamic environment.