Concept

Processus de décision markovien

Publications associées (162)

Optimal Information Collection Policies in a Markov Decision Process Framework

The cost-effectiveness and value of additional information about a health technology or program may change over time because of trends affecting patient cohorts and/or the intervention. Delaying information collection even for parameters that do not change ...

2018

Population-Level Intervention and Information Collection in Dynamic Healthcare Policy

Thomas Alois Weber

We develop a general framework for optimal health policy design in a dynamic setting. We consider a hypothetical medical intervention for a cohort of patients where one parameter varies across cohorts with imperfectly observable linear dynamics. We seek to ...

2018

Push recovery with stepping strategy based on time-projection control

Auke Ijspeert, Salman Faraji, Seyed Hamed Razavi

In this paper, we present a simple control framework for on-line push recovery with dynamic stepping properties. Due to relatively heavy legs in our robot, we need to take swing dynamics into account and thus use a linear model called 3LP which is composed ...

2018

The optimum is not the goal: Capturing the decision space for the planning of new neighborhoods

François Maréchal, Jessen Page, Nils Christian Schüler, Sébastien Cajot

This article presents the development and application of a methodology that employs optimization not to seek the single or few optimal plan(s) but to provide planners with a systematic overview of their decision space. As urban development projects are not ...

2018

Learning Continuous Muscle Control for a Multi-joint Arm by Extending Proximal Policy Optimization with a Liquid State Machine

Marc-Oliver Gewaltig

There have been many advances in the field of reinforcement learning in continuous control problems. Usually, these approaches use deep learning with artificial neural networks for approximation of policies and value functions. In addition, there have been ...

SPRINGER INTERNATIONAL PUBLISHING AG2018

Minimizing Regret in Unconstrained Online Convex Optimization

Maryam Kamgarpour

We consider online convex optimizations in the bandit setting. The decision maker does not know the time- varying cost functions, or their gradients. At each time step, she observes the value of the cost function for her chosen action. The objective is to ...

IEEE2018

Workshop Report: The Governance of Decision-Making Algorithms

Kujtesë Bejtullahu Michalopoulos

This report presents key interdisciplinary insights from IRGC’s expert workshop on the governance of decision-making algorithms, with particular focus on automated decisions based on learning algorithms (DMLAs). It highlights, among others, the need to imp ...

EPFL IRGC2018

Enforcing Model Adequacy in Real-Time Optimization via Dedicated Parameter Adaptation

Dominique Bonvin, Martand Singhal, Afaq Ahmad

Iterative real-time optimization schemes that employ modifier adaptation add bias and gradient correction terms to the model that is used for optimization. These affine corrections lead to meeting the first-order necessary conditions of optimality of the p ...

ELSEVIER SCIENCE BV2018

What to Choose Next? A Paradigm for Testing Human Sequential Decision Making

Michael Herzog, Aaron Michael Clarke, Elisa Tartaglia

Many of the decisions we make in our everyday lives are sequential and entail sparse rewards. While sequential decision-making has been extensively investigated in theory (e.g., by reinforcement learning models) there is no systematic experimental paradigm ...

2017

The Linear Programming Approach to Reach-Avoid Problems for Markov Decision Processes

Maryam Kamgarpour, John Lygeros, Tyler Summers

One of the most fundamental problems in Markov decision processes is analysis and control synthesis for safety and reachability specifications. We consider the stochastic reach-avoid problem, in which the objective is to synthesize a control policy to max ...

2017

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