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Model-free Reinforcement Learning for Control of Stochastic Discrete-time Systems
This paper proposes a reinforcement learning (RL) algorithm for infinite
horizon problem in a class of stochastic discrete-time
systems, rather than using a set of coupled generalized algebraic Riccati
equations (GAREs). The algorithm is able to learn the optimal control policy
for the system even when its parameters are unknown. Additionally, the paper
explores the effect of detection noise as well as the convergence of the
algorithm, and shows that the control policy is admissible after a finite
number of iterations. The algorithm is also able to handle multi-objective
control problems within stochastic fields. Finally, the algorithm is applied to
the F-16 aircraft autopilot with multiplicative noise
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