In the realm of supervised learning, Bayesian learning has shown robust predictive capabilities under input and parameter perturbations. Inspired by these findings, we demonstrate the robustness properties of Bayesian learning in the control search task. We seek to find a linear controller that stabilizes a one-dimensional open-loop unstable stochastic system. We compare two methods to deduce the controller: the first (deterministic) one assumes perfect knowledge of system parameter and state, the second takes into account uncertainties in both and employs Bayesian learning to compute a posterior distribution for the controller
This paper proposes a robust control design method using reinforcement learning for controlling part...
It is often assumed that agents in multiagent systems with state uncertainty have full knowledge of ...
Abstract-This paper introduces a learning-based robust control algorithm that provides robust stabil...
Applications to learn control of unfamiliar dynamical systems with increasing autonomy are ubiquitou...
This dissertation investigates modeling and control in a Bayesian setting. The methodology assumes t...
We consider the linear quadratic regulation problem when the plant is an unknown linear dynamical sy...
Control design for stochastic uncertain nonlinear systems is traditionally based on minimizing the e...
For dynamical systems with uncertainty, robust controllers can be designed by assuming that the unce...
For autonomous robots, we propose an approximate model-based Bayesian reinforcement learning (MB-BRL...
Data-driven approaches to the design of control policies for robotic systems have the potential to r...
Classic inventory control problems typically assume that the demand distribution is known a priori. ...
We study Bayesian optimal control of a general class of smoothly parameterized Markov deci-sion prob...
Robotic setups often need fine-tuned controller parameters both at low- and task-levels. Finding an ...
This thesis develops various methods for the robust and stochastic model-based control of uncertain ...
Despite the success of reinforcement learning (RL) in various research fields, relatively few algori...
This paper proposes a robust control design method using reinforcement learning for controlling part...
It is often assumed that agents in multiagent systems with state uncertainty have full knowledge of ...
Abstract-This paper introduces a learning-based robust control algorithm that provides robust stabil...
Applications to learn control of unfamiliar dynamical systems with increasing autonomy are ubiquitou...
This dissertation investigates modeling and control in a Bayesian setting. The methodology assumes t...
We consider the linear quadratic regulation problem when the plant is an unknown linear dynamical sy...
Control design for stochastic uncertain nonlinear systems is traditionally based on minimizing the e...
For dynamical systems with uncertainty, robust controllers can be designed by assuming that the unce...
For autonomous robots, we propose an approximate model-based Bayesian reinforcement learning (MB-BRL...
Data-driven approaches to the design of control policies for robotic systems have the potential to r...
Classic inventory control problems typically assume that the demand distribution is known a priori. ...
We study Bayesian optimal control of a general class of smoothly parameterized Markov deci-sion prob...
Robotic setups often need fine-tuned controller parameters both at low- and task-levels. Finding an ...
This thesis develops various methods for the robust and stochastic model-based control of uncertain ...
Despite the success of reinforcement learning (RL) in various research fields, relatively few algori...
This paper proposes a robust control design method using reinforcement learning for controlling part...
It is often assumed that agents in multiagent systems with state uncertainty have full knowledge of ...
Abstract-This paper introduces a learning-based robust control algorithm that provides robust stabil...
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