Efficient reinforcement learning for fluid flow control

Despite the low dimensionalities of dissipative viscous fluids, reinforcement learning (RL) requires many observables in fluid control problems. This is because the observables are assumed to follow a policy-independent Markov decision process in the RL framework. By including policy parameters as arguments of a value function, we construct a consistent algorithm with partial observables. Using typical examples of fluid control around a cylinder, we show that our algorithm is more stable and efficient than the existing RL algorithms, even under a small number of observations.