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» A Variance Analysis for POMDP Policy Evaluation
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AAAI
2008
10 years 6 months ago
A Variance Analysis for POMDP Policy Evaluation
Partially Observable Markov Decision Processes have been studied widely as a model for decision making under uncertainty, and a number of methods have been developed to find the s...
Mahdi Milani Fard, Joelle Pineau, Peng Sun
NIPS
2008
10 years 5 months ago
Particle Filter-based Policy Gradient in POMDPs
Our setting is a Partially Observable Markov Decision Process with continuous state, observation and action spaces. Decisions are based on a Particle Filter for estimating the bel...
Pierre-Arnaud Coquelin, Romain Deguest, Rém...
ATAL
2009
Springer
10 years 10 months ago
SarsaLandmark: an algorithm for learning in POMDPs with landmarks
Reinforcement learning algorithms that use eligibility traces, such as Sarsa(λ), have been empirically shown to be effective in learning good estimated-state-based policies in pa...
Michael R. James, Satinder P. Singh
NIPS
2008
10 years 5 months ago
Signal-to-Noise Ratio Analysis of Policy Gradient Algorithms
Policy gradient (PG) reinforcement learning algorithms have strong (local) convergence guarantees, but their learning performance is typically limited by a large variance in the e...
John W. Roberts, Russ Tedrake
PROMAS
2004
Springer
10 years 9 months ago
Coordinating Teams in Uncertain Environments: A Hybrid BDI-POMDP Approach
Distributed partially observable Markov decision problems (POMDPs) have emerged as a popular decision-theoretic approach for planning for multiagent teams, where it is imperative f...
Ranjit Nair, Milind Tambe
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