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Taming Lagrangian Chaos with Multi-Objective Reinforcement Learning
We consider the problem of two active particles in 2D complex flows with the multi-objective goals of minimizing both the dispersion rate and the energy consumption of the pair.
Reinforcement learning for pursuit and evasion of microswimmers at low Reynolds number
We consider a model of two competing microswimming agents engaged in a pursue-evasion task within a low-Reynolds-number environment.
Flow Navigation by Smart Microswimmers via Reinforcement Learning
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.
Infomax strategies for an optimal balance between exploration and exploitation
Proper balance between exploitation and exploration is what makes good decisions, which achieve high rewards like payoff or evolutionary fitness.
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