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ICML
1994
IEEE

A Modular Q-Learning Architecture for Manipulator Task Decomposition

13 years 7 months ago
A Modular Q-Learning Architecture for Manipulator Task Decomposition
Compositional Q-Learning (CQ-L) (Singh 1992) is a modular approach to learning to performcomposite tasks made up of several elemental tasks by reinforcement learning. Skills acquired while performing elemental tasks are also applied to solve composite tasks. Individual skills compete for the right to act and only winning skills are included in the decomposition of the composite task. We extend the original CQ-L concept in two ways: (1) a more general reward function, and (2) the agent can have more than one actuator. We use the CQ-L architecture to acquire skills for performing composite tasks with a simulated twolinked manipulator having large state and action spaces. The manipulator is a non-linear dynamical system and we require its end-effector to be at specific positions in the workspace. Fast function approximation in each of the Q-modules is achieved through the use of an array of Cerebellar Model Articulation Controller (CMAC) (Albus 1975) structures.
Chen K. Tham, Richard W. Prager
Added 27 Aug 2010
Updated 27 Aug 2010
Type Conference
Year 1994
Where ICML
Authors Chen K. Tham, Richard W. Prager
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