Estimation and control using sampling-based Bayesian reinforcement learning

Real-world autonomous systems operate under uncertainty about both their pose and dynamics. Autonomous control systems must simultaneously perform estimation and control tasks to maintain robustness to changing dynamics or modelling errors. However, information gathering actions often conflict with...

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Hlavní autoři:	Patrick Slade, Zachary N. Sunberg, Mykel J. Kochenderfer
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Wiley 2019-12-01
Edice:	IET Cyber-Physical Systems
Témata:	linear systems tree searching path planning belief networks bayes methods discrete time systems decision theory stochastic systems monte carlo methods kalman filters markov processes mobile robots probability predictive control optimisation uncertain systems real-world autonomous systems pose dynamics autonomous control systems control tasks changing dynamics modelling errors information gathering actions optimal actions control objectives discrete-time nonlinear systems process noise input-constraints parameter uncertainty bayes-adaptive markov decision process unscented kalman filter certainty equivalent model predictive control discrete time simulations offline optimisation method monte carlo tree search parameters sampling-based bayesian reinforcement learning recursive feasibility guarantees probabilistic bounding heuristic qmdp solution
On-line přístup:	https://digital-library.theiet.org/content/journals/10.1049/iet-cps.2019.0045
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Estimation and control using sampling-based Bayesian reinforcement learning

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