Concept

Markov decision process

Related publications (162)

Human and Machine Learning in Non-Markovian Decision Making

Michael Herzog, Aaron Michael Clarke, Elisa Tartaglia, Silvia Marchesotti, Walter Senn

Humans can learn under a wide variety of feedback conditions. Reinforcement learning (RL), where a series of rewarded decisions must be made, is a particularly important type of learning. Computational and behavioral studies of RL have focused mainly on Ma ...

Public Library of Science2015

Learning search polices from humans in a partially observable context

Aude Billard, Guillaume Pierre Luc De Chambrier

Decision making and planning for which the state information is only partially available is a problem faced by all forms of intelligent entities they being either virtual, synthetic or biological. The standard approach to mathematically solve such a decisi ...

2014

Convex computation of the maximum controlled invariant set for polynomial control systems

Colin Neil Jones, Milan Korda

We characterize the maximum controlled invariant (MCI) set for discrete- as well as continuous-time nonlinear dynamical systems as the solution of an infinite-dimensional linear programming problem. For systems with polynomial dynamics and compact semialge ...

Society for Industrial and Applied Mathematics2014

The value of updated information on future price behaviour

Ralf Seifert, Shuangqing Liao

This paper studies the value of information on future price behaviour. We consider a one-period inventory modelling framework with random period length and two order opportunities. The selling price is determined dynamically and the demand is price-sensiti ...

Taylor & Francis Ltd2014

Coupling Movement Primitives: Interaction With the Environment and Bimanual Tasks

Auke Ijspeert

The framework of dynamic movement primitives (DMPs) contains many favorable properties for the execution of robotic trajectories, such as indirect dependence on time, response to perturbations, and the ability to easily modulate the given trajectories, but ...

Ieee-Inst Electrical Electronics Engineers Inc2014

A stochastic games framework for verification and control of discrete time stochastic hybrid systems

Maryam Kamgarpour, John Lygeros

We describe a framework for analyzing probabilistic reachability and safety problems for discrete time stochastic hybrid systems within a dynamic games setting. In particular, we consider finite horizon zero-sum stochastic games in which a control has the ...

2013

The Entropy of Conditional Markov Trajectories

Patrick Thiran, Matthias Grossglauser, Mohamed Kafsi

To quantify the randomness of Markov trajectories with fixed initial and final states, Ekroot and Cover proposed a closed-form expression for the entropy of trajectories of an irreducible finite state Markov chain. Numerous applications, including the stud ...

2013

Stochastic system controller synthesis for reachability specifications encoded by random sets

Maryam Kamgarpour, John Lygeros

We consider a reach–avoid specification for a stochastic hybrid dynamical system defined as reaching a goal set at some finite time, while avoiding an unsafe set at all previous times. In contrast with earlier works which consider the target and avoid sets ...

2013

Utility Based State Learning for Controlling Partially Observable Gene Regulatory Networks

Utku Sirin

The external control of Gene Regulatory Networks (GRNs) has received much attention in recent years. In this paper, we propose a novel algorithm for controlling partially observable GRNs coupling utility based state learning methods and Batch Mode Reinforc ...

2013

Robust Markov Decision Processes

Daniel Kuhn, Wolfram Wiesemann

Markov decision processes (MDPs) are powerful tools for decision making in uncertain dynamic environments. However, the solutions of MDPs are of limited practical use because of their sensitivity to distributional model parameters, which are typically unkn ...

2013

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