TY - JOUR
AU - Shimizu, Masaki
AB - Abstract: 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.
TI - Efficient reinforcement learning with partially observable for fluid flow control
JF - Physics
DA - 2020-12-08
UR - https://www.deepdyve.com/lp/arxiv-cornell-university/efficient-reinforcement-learning-with-partially-observable-for-fluid-5JIHU8cJMY
VL - 2021
IS - 2012
DP - DeepDyve
ER -