Results 271 to 280 of about 30,788 (321)

Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control. [PDF]

Sensors (Basel)
Gutiérrez-Moreno R, Barea R, López-Guillén E, Arango F, Sánchez-García F, Bergasa LM.   +5 more
europepmc   +1 more source

The performance of drones and artificial intelligence for monitoring sage‐grouse at leks

Wildlife Biology, EarlyView.
Accurately monitoring sage‐grouse populations is critical for conservation, yet traditional ground‐based visual surveys face challenges in scalability and consistency, prompting the exploration of innovative drone‐based methodologies enhanced by artificial intelligence.
Lance B. McNew, Jason Hanlon, Ilya Buzytsky   +2 more
wiley   +1 more source

Causal machine learning methods for understanding land use and land cover change. [PDF]

Landsc Ecol
Eigenbrod F, Alexander P, Apfel N, Athanasiadis IN, Berger T, Bullock JM, Duveiller G, Equihua J, Menezes I, Moreira R, Paudel D, Sitokonstantinou V, Reichstein M, Willcock S, Woodman T.   +14 more
europepmc   +1 more source

Learning Partially Observable Markov Decision Processes Using Abstract Actions

, 2014
Hamed Janzadeh
openalex   +1 more source

Flips reveal the universal impact of memory on random explorations. [PDF]

Nat Commun
Brémont J, Régnier L, Barbier-Chebbah A, Voituriez R, Bénichou O.   +4 more
europepmc   +1 more source

Federated reinforcement learning with constrained markov decision processes and graph neural networks for fair and grid-constrained coordination of large-scale electric vehicle charging networks. [PDF]

Sci Rep
Zhou L, Huo D, Chen J, Bo B, Li H.
europepmc   +1 more source

Free Energy Projective Simulation (FEPS): Active inference with interpretability. [PDF]

PLoS One
Pazem J, Krumm M, Vining AQ, Fiderer LJ, Briegel HJ.   +4 more
europepmc   +1 more source

Machine learning for estimation and control of quantum systems. [PDF]

Natl Sci Rev
Ma H, Qi B, Petersen IR, Wu RB, Rabitz H, Dong D.   +5 more
europepmc   +1 more source

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Partially Observable Markov Decision Processes

2011
In many applications the decision maker has only partial information about the state process, i.e. part of the state cannot be observed. Examples can be found in engineering, economics, statistics, speech recognition and learning theory among others. An important financial application is given when the drift of a stock price process is unobservable and
Nicole Bäuerle, Ulrich Rieder
openaire   +2 more sources

Partially Observable Markov Decision Processes

2020
This chapter covers Partially Observable Markov Decision Processes (POMDPs), that extend MDPs for when the state is not completely observable. After a general introduction to POMDPs, their formal representation and properties are described. The representation of the value function as a set of linear equations (\(\alpha -vectors\)) is presented via a ...
openaire   +1 more source