651 search results - page 39 / 131 » Algorithms for Inverse Reinforcement Learning |
NIPS
2003
13 years 10 months ago
2003
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learning in continuous state spaces and discrete time. We demonstrate how the GP mod...
FLAIRS
1998
13 years 10 months ago
1998
Manyindustrial processes involve makingparts with an assemblyof machines, where each machinecarries out an operation on a part, and the finished product requires a wholeseries of ...
GECCO
2005
Springer
14 years 2 months ago
2005
Springer
Recurrent neural networks are theoretically capable of learning complex temporal sequences, but training them through gradient-descent is too slow and unstable for practical use i...
ICRA
2005
IEEE
14 years 2 months ago
2005
IEEE
— This paper presents a new reinforcement learning algorithm for accelerating acquisition of new skills by real mobile robots, without requiring simulation. It speeds up Q-learni...
CORR
2010
Springer
13 years 7 months ago
2010
Springer
Gaussian graphical models are of great interest in statistical learning. Because the conditional independencies between different nodes correspond to zero entries in the inverse c...