swMATH ID: 3055
Software Authors: Ross, S.; Pineau, J.; Paquet, S.; Chaib-Draa, B.
Description: 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 execution. Online algorithms generally consist of a lookahead search to find the best action to execute at each time step in an environment. Our objectives here are to survey the various existing online POMDP methods, analyze their properties and discuss their advantages and disadvantages; and to thoroughly evaluate these online approaches in different environments under various metrics (return, error bound reduction, lower bound improvement). Our experimental results indicate that state-of-the-art online heuristic search methods can handle large POMDP domains efficiently
Homepage: http://www.pomdp.org/pomdp/index.shtml
Keywords: Partially Observable Markov Decision Processes; online algorithms
Related Software: DESPOT; EGO; PRMLT; Clingo; Anglican; ProbLog; GOLOG; CARLA; AdaGrad; Adam; Approxrl; ElemStatLearn; ROME; COMPASS; itsmr; BayesDA; R-MAX; MCQueue; UCPOP; Graphplan
Cited in: 38 Publications
Further Publications: http://www.pomdp.org/pomdp/papers/index.shtml

Standard Articles

1 Publication describing the Software, including 1 Publication in zbMATH Year
Online planning algorithms for POMDPS. Zbl 1182.68265
Ross, S.; Pineau, J.; Paquet, S.; Chaib-Draa, B.

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