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JMLR
2012
13 years 10 months ago
Hierarchical Relative Entropy Policy Search
Many real-world problems are inherently hierarchically structured. The use of this structure in an agent’s policy may well be the key to improved scalability and higher performa...
Christian Daniel, Gerhard Neumann, Jan Peters
ICML
2010
IEEE
15 years 8 months ago
From Transformation-Based Dimensionality Reduction to Feature Selection
Many learning applications are characterized by high dimensions. Usually not all of these dimensions are relevant and some are redundant. There are two main approaches to reduce d...
Mahdokht Masaeli, Glenn Fung, Jennifer G. Dy
ICML
2004
IEEE
16 years 1 months ago
Learning a kernel matrix for nonlinear dimensionality reduction
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into ...
Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul
GECCO
2006
Springer
208views Optimization» more  GECCO 2006»
15 years 11 months ago
Comparing evolutionary and temporal difference methods in a reinforcement learning domain
Both genetic algorithms (GAs) and temporal difference (TD) methods have proven effective at solving reinforcement learning (RL) problems. However, since few rigorous empirical com...
Matthew E. Taylor, Shimon Whiteson, Peter Stone
GECCO
2000
Springer
143views Optimization» more  GECCO 2000»
15 years 11 months ago
A Genetic Algorithm for Automatically Designing Modular Reinforcement Learning Agents
Reinforcement learning (RL) is one of the machine learning techniques and has been received much attention as a new self-adaptive controller for various systems. The RL agent auto...
Isao Ono, Tetsuo Nijo, Norihiko Ono