Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints
CP Andriotis, KG Papakonstantinou - Reliability Engineering & System …, 2021 - Elsevier
Determination of inspection and maintenance policies for minimizing long-term risks and
costs in deteriorating engineering environments constitutes a complex optimization problem …
costs in deteriorating engineering environments constitutes a complex optimization problem …
Online tree-based planning for active spacecraft fault estimation and collision avoidance
Autonomous robots operating in uncertain or hazardous environments subject to state safety
constraints must be able to identify and isolate faulty components in a time-optimal manner …
constraints must be able to identify and isolate faulty components in a time-optimal manner …
Learning with safety constraints: Sample complexity of reinforcement learning for constrained mdps
Many physical systems have underlying safety considerations that require that the policy
employed ensures the satisfaction of a set of constraints. The analytical formulation usually …
employed ensures the satisfaction of a set of constraints. The analytical formulation usually …
Monte-Carlo tree search for constrained POMDPs
Abstract Monte-Carlo Tree Search (MCTS) has been successfully applied to very large
POMDPs, a standard model for stochastic sequential decision-making problems. However …
POMDPs, a standard model for stochastic sequential decision-making problems. However …
Rao*: An algorithm for chance-constrained pomdp's
S Thiébaux, B Williams - Proceedings of the AAAI Conference on …, 2016 - ojs.aaai.org
Autonomous agents operating in partially observable stochastic environments often face the
problem of optimizing expected performance while bounding the risk of violating safety …
problem of optimizing expected performance while bounding the risk of violating safety …
Constrained multiagent Markov decision processes: A taxonomy of problems and algorithms
In domains such as electric vehicle charging, smart distribution grids and autonomous
warehouses, multiple agents share the same resources. When planning the use of these …
warehouses, multiple agents share the same resources. When planning the use of these …
Approximate linear programming for constrained partially observable Markov decision processes
In many situations, it is desirable to optimize a sequence of decisions by maximizing a
primary objective while respecting some constraints with respect to secondary objectives …
primary objective while respecting some constraints with respect to secondary objectives …
Online planning for constrained POMDPs with continuous spaces through dual ascent
Rather than augmenting rewards with penalties for undesired behavior, Constrained
Partially Observable Markov Decision Processes (CPOMDPs) plan safely by imposing …
Partially Observable Markov Decision Processes (CPOMDPs) plan safely by imposing …
Cognitive radar for classification
S Brüggenwirth, M Warnke, S Wagner… - IEEE Aerospace and …, 2019 - ieeexplore.ieee.org
The article presents the cognitive radar architecture of Fraunhofer FHR based on a three-
layer model of human cognitive performance. The approach is illustrated using examples for …
layer model of human cognitive performance. The approach is illustrated using examples for …
Safe reinforcement learning from pixels using a stochastic latent representation
We address the problem of safe reinforcement learning from pixel observations. Inherent
challenges in such settings are (1) a trade-off between reward optimization and adhering to …
challenges in such settings are (1) a trade-off between reward optimization and adhering to …