Searching for authors named "Pierre Laroche" – sorted by Relevance.
5 documents found, showing 1 through 5.
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Building Efficient Partial Plans using Markov Decision Processes
- …Markov Decision Processes (MDP) have been widely used as a framework for planning under uncertainty. They allow to compute optimal sequences of actions in order to achieve a given goal, accounting for actuators uncertainties. But algorithms classically used to solve MDPs are intractable for problems…
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State Aggregation for Solving Markov Decision Problems An Application to Mobile Robotics
- …In this paper, we present two state aggregation methods, used to build stochastic plans, modelling our environment with Markov Decision Processes. Classical methods used to compute stochastic plans are highly untractable for problems necessiting a large number of states, like our robotics applicatio…
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Decomposition of Markov Decision Processes Using Directed Graphs
- …Introduction Decomposing an MDP consists in (1) partitioning the state space into regions; (2) solving each region independently; (3) combining the local solutions to generate a global one. [Dean and Lin1995] give a general framework to decompose MDPs, but the process they propose is quite complex.…
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Mobile Robot Localization in Dynamic Environment using Places Recognition
- …In this paper, we present a new method to localize a mobile robot in dynamic environments. This method is based on places recognition, and a match between places recognized and the sequence of places that the mobile robot is able to see during a run from an initial place to an ending place. Our meth…
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Decomposition of Markov Decision Processes Using Directed Graphs
- …. As classical methods are intractable for solving Markov Decision Processes requiring a large state space, decomposition and aggregation techniques are very useful to cope with large problems. Once the space has been partitionned into regions, each region is solved independently, and the local …
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