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ML
2002
ACM
114views Machine Learning» more  ML 2002»
14 years 9 months ago
Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts
The execution order of a block of computer instructions on a pipelined machine can make a difference in running time by a factor of two or more. Compilers use heuristic schedulers...
Amy McGovern, J. Eliot B. Moss, Andrew G. Barto
IJCM
2008
93views more  IJCM 2008»
14 years 9 months ago
A reinforced learning control using iterative error compensation for uncertain dynamical systems
This paper investigates a learning control using iterative error compensation for uncertain systems to enhance the precision of high speed, computer controlled machining process. ...
Kuei-Shu Hsu, Wen-Shyong Yu, Ming-In Ho
FLAIRS
1998
14 years 11 months ago
Optimizing Production Manufacturing Using Reinforcement Learning
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 ...
Sridhar Mahadevan, Georgios Theocharous
85
Voted
ICML
2005
IEEE
15 years 10 months ago
Identifying useful subgoals in reinforcement learning by local graph partitioning
We present a new subgoal-based method for automatically creating useful skills in reinforcement learning. Our method identifies subgoals by partitioning local state transition gra...
Özgür Simsek, Alicia P. Wolfe, Andrew G....
58
Voted
AAAI
1997
14 years 11 months ago
Machine Learning for Intelligent Systems
Recent research in machine learning has focused on supervised induction for simple classi cation and reinforcement learning for simple reactive behaviors. In the process, the eld ...
Pat Langley