Partially observable markov decision process

Results 191 to 200 of about 35,395 (320)

TMAC: a Transformer-based partially observable multi-agent communication method. [PDF]

PeerJ Comput Sci
Li X, Xue S, He Z, Shi H.
europepmc +1 more source

Extending the hyper‐logistic model to the random setting: New theoretical results with real‐world applications

Mathematical Methods in the Applied Sciences, EarlyView.
We develop a full randomization of the classical hyper‐logistic growth model by obtaining closed‐form expressions for relevant quantities of interest, such as the first probability density function of its solution, the time until a given fixed population is reached, and the population at the inflection point.
Juan Carlos Cortés +2 more
wiley +1 more source

Overview Study of Partially Observable Hidden Markov Models for Ambient Movement Guidance Support. [PDF]

Int J Telemed Appl
Payandeh S.
europepmc +1 more source

Online Learning Strategy Induction through Partially Observable Markov Decision Process-Based Cognitive Experience Model

Huifan Gao, Biyang Ma
openalex +1 more source

Minimal Disclosure in Partially Observable Markov Decision Processes

, 2011
Nathalie Bertrand, Blaise Genest
openalex +2 more sources

Hybrid Reaction–Diffusion Epidemic Models: Dynamics and Emergence of Oscillations

Mathematical Methods in the Applied Sciences, EarlyView.
ABSTRACT In this paper, we construct a hybrid epidemic mathematical model based on a reaction–diffusion system of the SIR (susceptible‐infected‐recovered) type. This model integrates the impact of random factors on the transmission rate of infectious diseases, represented by a probabilistic process acting at discrete time steps.
Asmae Tajani, Cristiana J. Silva, Guillaume Cantin +2 more
wiley +1 more source

Optimal experimental design for partially observable pure birth processes. [PDF]

PLoS One
Eshragh A, Skerritt MP, Salvy B, McCallum T. +3 more
europepmc +1 more source

Employing decomposable partially observable Markov decision processes to control gene regulatory networks

, 2017
Utku Erdoğdu, Faruk Polat, Reda Alhajj
openalex +2 more sources

Reinforcement Learning in Partially Observable Markov Decision Processes using Hybrid Probabilistic Logic Programs [PDF]

, 2010
Emad Saad
openalex +1 more source

Approximation of Discontinuous Functions by Positive Linear Operators. A Probabilistic Approach

Mathematical Methods in the Applied Sciences, EarlyView.
ABSTRACT We obtain approximation results for general positive linear operators satisfying mild conditions, when acting on discontinuous functions and absolutely continuous functions having discontinuous derivatives. The upper bounds, given in terms of a local first modulus of continuity, are best possible, in the sense that we can construct particular ...
J.A. Adell +2 more
wiley +1 more source

markov and semi-markov decision processes
deep reinforcement learning
dynamic programming

pomdp
markov decision process
fos: computer and information sciences

reinforcement learning
artificial intelligence cs.ai
partially observable markov decision processes