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AAAI
2007
13 years 9 months ago
Scaling Up: Solving POMDPs through Value Based Clustering
Partially Observable Markov Decision Processes (POMDPs) provide an appropriately rich model for agents operating under partial knowledge of the environment. Since finding an opti...
Yan Virin, Guy Shani, Solomon Eyal Shimony, Ronen ...
UAI
2000
13 years 8 months ago
PEGASUS: A policy search method for large MDPs and POMDPs
We propose a new approach to the problem of searching a space of policies for a Markov decision process (MDP) or a partially observable Markov decision process (POMDP), given a mo...
Andrew Y. Ng, Michael I. Jordan
ICTAI
2010
IEEE
13 years 5 months ago
A Closer Look at MOMDPs
Abstract--The difficulties encountered in sequential decisionmaking problems under uncertainty are often linked to the large size of the state space. Exploiting the structure of th...
Mauricio Araya-López, Vincent Thomas, Olivi...
NIPS
2008
13 years 9 months ago
Bayesian Model of Behaviour in Economic Games
Classical game theoretic approaches that make strong rationality assumptions have difficulty modeling human behaviour in economic games. We investigate the role of finite levels o...
Debajyoti Ray, Brooks King-Casas, P. Read Montague...
CORR
2010
Springer
105views Education» more  CORR 2010»
13 years 6 months ago
Optimism in Reinforcement Learning Based on Kullback-Leibler Divergence
We consider model-based reinforcement learning in finite Markov Decision Processes (MDPs), focussing on so-called optimistic strategies. Optimism is usually implemented by carryin...
Sarah Filippi, Olivier Cappé, Aurelien Gari...