A Concise Introduction to Models and Methods for Automated PlanningMDP Planning: Stochastic Actions and Full Feedback
A Concise Introduction to Models and Methods for Automated Planning: MDP Planning: Stochastic...
Geffner, Hector; Bonet, Blai
2013-01-01 00:00:00
[Markov Decision Processes (MDPs) generalize the model underlying classical planning by allowing actions with stochastic effects and fully observable states. In this chapter, we look at a variety of MDP models and the basic algorithms for solving them: from offline methods based on dynamic programming and heuristic search, to online methods where the action to do next is obtained by solving simplifications, like finite-horizon versions of the problem or deterministic relaxations.]
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A Concise Introduction to Models and Methods for Automated PlanningMDP Planning: Stochastic Actions and Full Feedback
[Markov Decision Processes (MDPs) generalize the model underlying classical planning by allowing actions with stochastic effects and fully observable states. In this chapter, we look at a variety of MDP models and the basic algorithms for solving them: from offline methods based on dynamic programming and heuristic search, to online methods where the action to do next is obtained by solving simplifications, like finite-horizon versions of the problem or deterministic relaxations.]
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