Abstract: This paper presents an iterative linear-quadratic-Gaussian method for locally-optimal control and estimation of nonlinear stochastic systems. The new method constructs an affine feedback ...
Abstract: Optimal control and estimation are dual in the LQG setting, as Kalman discovered, however this duality has proven difficult to extend beyond LQG. Here we obtain a more natural form of LQG ...
The motor system can be considered at three levels: motor behaviour, limb mechanics and neural control. Although our understanding at each level continues to grow, linking these levels into a cohesive ...
ABSTRACT: We present an optimal control model of three stages of resources allocation for managing invasive species. Three types of temporal uncertainty are considered, involving the timing of ...
The first task requires using EKF to obtain the state of the bipedal robot XiaoTian as the input of RL. The second task requires the use of RL to train the inverted pendulum to remain vertically ...
Deep reinforcement learning (DRL) is applied to control a nonlinear, chaotic system governed by the one-dimensional Kuramoto–Sivashinsky (KS) equation. DRL uses reinforcement learning principles for ...
Drug use and related problems change substantially over time, so it seems plausible that drug interventions should vary too. To investigate this possibility, we set up a continuous time version of the ...
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