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» Composing and combining policies under the policy machine
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ICML
2003
IEEE
15 years 10 months ago
Hierarchical Policy Gradient Algorithms
Hierarchical reinforcement learning is a general framework which attempts to accelerate policy learning in large domains. On the other hand, policy gradient reinforcement learning...
Mohammad Ghavamzadeh, Sridhar Mahadevan
80
Voted
ICML
2009
IEEE
15 years 10 months ago
Binary action search for learning continuous-action control policies
Reinforcement Learning methods for controlling stochastic processes typically assume a small and discrete action space. While continuous action spaces are quite common in real-wor...
Jason Pazis, Michail G. Lagoudakis
ICAC
2008
IEEE
15 years 4 months ago
Generating Adaptation Policies for Multi-tier Applications in Consolidated Server Environments
Creating good adaptation policies is critical to building complex autonomic systems since it is such policies that define the system configuration used in any given situation. W...
Gueyoung Jung, Kaustubh R. Joshi, Matti A. Hiltune...
ICML
1995
IEEE
15 years 10 months ago
Learning Policies for Partially Observable Environments: Scaling Up
Partially observable Markov decision processes (pomdp's) model decision problems in which an agent tries to maximize its reward in the face of limited and/or noisy sensor fee...
Michael L. Littman, Anthony R. Cassandra, Leslie P...
90
Voted
AAAI
2011
13 years 9 months ago
User-Controllable Learning of Location Privacy Policies With Gaussian Mixture Models
With smart-phones becoming increasingly commonplace, there has been a subsequent surge in applications that continuously track the location of users. However, serious privacy conc...
Justin Cranshaw, Jonathan Mugan, Norman M. Sadeh