• Publications
  • Influence
Perseus: Randomized Point-based Value Iteration for POMDPs
TLDR
This work presents a randomized point-based value iteration algorithm called PERSEUS, which backs up only a (randomly selected) subset of points in the belief set, sufficient for improving the value of each belief point in the set. Expand
Optimal and Approximate Q-value Functions for Decentralized POMDPs
TLDR
This paper studies whether similar Q-value functions can be defined for decentralized POMDP models (Dec-POMDPs), and how policies can be extracted from such value functions, and describes a family of algorithms for extracting policies from such Q- value functions. Expand
Point-Based Value Iteration for Continuous POMDPs
TLDR
It is demonstrated that the value function for continuous POMDPs is convex in the beliefs over continuous state spaces, and piecewise-linear convex for the particular case of discrete observations and actions but still continuous states. Expand
A point-based POMDP algorithm for robot planning
  • M. Spaan, N. Vlassis
  • Computer Science
  • IEEE International Conference on Robotics and…
  • 6 July 2004
TLDR
A simple, randomized procedure that performs value update steps that strictly improve the value of all belief points in each step that belongs to the family of point-based value iteration solution techniques for POMDP. Expand
Interaction-driven Markov games for decentralized multiagent planning under uncertainty
TLDR
A fast approximate solution method for planning in IDMGs is introduced, exploiting their particular structure, and its successful application on several large multiagent tasks is illustrated. Expand
Partially Observable Markov Decision Processes
  • M. Spaan
  • Computer Science
  • Reinforcement Learning
  • 2012
TLDR
This chapter presents the POMDP model by focusing on the differences with fully observable MDPs, and it is shown how optimal policies for POM DPs can be represented. Expand
Scaling Up Optimal Heuristic Search in Dec-POMDPs via Incremental Expansion
TLDR
A key insight is that one can avoid the full expansion of a search node that generates a number of children that is doubly exponential in the node's depth, allowing for optimal solutions over longer horizons for many benchmark problems. Expand
Lossless clustering of histories in decentralized POMDPs
TLDR
This work proves that when two histories satisfy the criterion, they have the same optimal value and thus can be treated as one, and demonstrates empirically that it can provide a speed-up of multiple orders of magnitude, allowing the optimal solution of significantly larger problems. Expand
Accelerated Vector Pruning for Optimal POMDP Solvers
TLDR
This paper shows how the LPs in POMDP pruning subroutines can be decomposed using a Benders decomposition and shows that the resulting algorithm incrementally adds LP constraints and uses only a small fraction of the constraints. Expand
Decentralized multi-robot cooperation with auctioned POMDPs
TLDR
This paper proposes to decentralize multiagent Partially Observable Markov Decision Process (POMDPs) while maintaining cooperation between robots by using POMDP policy auctions by applying a decentralized data fusion method in order to efficiently maintain a joint belief state among the robots. Expand
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