280 search results - page 46 / 56 » Planning for Markov Decision Processes with Sparse Stochasti... |
ICRA
2010
IEEE
14 years 8 months ago
2010
IEEE
— Partially Observable Markov Decision Processes (POMDPs) offer a powerful mathematical framework for making optimal action choices in noisy and/or uncertain environments, in par...
108
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IJRR
2011
14 years 4 months ago
2011
Abstract Partially observable Markov decision processes (POMDPs) are a principled mathematical framework for planning under uncertainty, a crucial capability for reliable operation...
NIPS
2008
14 years 11 months ago
2008
Markov Decision Processes (MDPs) have been extensively studied and used in the context of planning and decision-making, and many methods exist to find the optimal policy for probl...
AIPS
2000
14 years 11 months ago
2000
1 We consider the problem of scheduling an unknown sequence of tasks for a single server as the tasks arrive with the goal off maximizing the total weighted value of the tasks serv...
ECML
2007
Springer
15 years 4 months ago
2007
Springer
We present Policy Gradient Actor-Critic (PGAC), a new model-free Reinforcement Learning (RL) method for creating limited-memory stochastic policies for Partially Observable Markov ...