Online Planning Algorithms for POMDPs
نویسندگان
چکیده
منابع مشابه
Online Planning Algorithms for POMDPs
Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains. However, solving a POMDP is often intractable except for small problems due to their complexity. Here, we focus on online approaches that alleviate the computational complexity by computing good local policies at each decision step during the e...
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Current point-based planning algorithms for solving partially observable Markov decision processes (POMDPs) have demonstrated that a good approximation of the value function can be derived by interpolation from the values of a specially selected set of points. The performance of these algorithms can be improved by eliminating unnecessary backups or concentrating on more important points in the ...
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We describe an approximate dynamic programming algorithm for partially observable Markov decision processes represented in factored form. Two complementary forms of approximation are used to simplify a piecewise linear and convex value function, where each linear facet of the function is represented compactly by an algebraic decision diagram. ln one form of approximation, the degree of state ab...
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Online, sample-based planning algorithms for POMDPs have shown great promise in scaling to problems with large state spaces, but they become intractable for large action and observation spaces. This is particularly problematic in multiagent POMDPs where the action and observation space grows exponentially with the number of agents. To combat this intractability, we propose a novel scalable appr...
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Although partially observable Markov decision processes (POMDPs) have received significant attention in past years, to date, solving problems of realistic order of magnitude remains a serious challenge. In this context, techniques that accelerate fundamental algorithms have been a main focus of research. Among them prioritized solvers suggest solutions to the problem of ordering backup operatio...
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ژورنال
عنوان ژورنال: Journal of Artificial Intelligence Research
سال: 2008
ISSN: 1076-9757
DOI: 10.1613/jair.2567