32 search results - page 3 / 7 » Learning Policies for Partially Observable Environments: Sca... |
IJRR
2010
13 years 6 months ago
2010
Partially observable Markov decision processes (POMDPs) provide a principled, general framework for robot motion planning in uncertain and dynamic environments. They have been app...
ICRA
2007
IEEE
14 years 1 months ago
2007
IEEE
— While the Partially Observable Markov Decision Process (POMDP) provides a formal framework for the problem of robot control under uncertainty, it typically assumes a known and ...
ECML
2007
Springer
14 years 1 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 ...
IAT
2005
IEEE
14 years 1 months ago
2005
IEEE
Partially observable Markov decision process (POMDP) is commonly used to model a stochastic environment with unobservable states for supporting optimal decision making. Computing ...
LCTRTS
2007
Springer
14 years 1 months ago
2007
Springer
Embedded systems serve an emerging and diverse set of applications. As a result, more computational and storage capabilities are added to accommodate ever more demanding applicati...