  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
partially observable condition. Using typical examples of active flow control,
we show that our algorithm is more stable and efficient than the existing RL
algorithms, even under a small number of observables.
