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ICML
2000
IEEE
14 years 8 months ago
Reinforcement Learning in POMDP's via Direct Gradient Ascent
This paper discusses theoretical and experimental aspects of gradient-based approaches to the direct optimization of policy performance in controlled ??? ?s. We introduce ??? ?, a...
Jonathan Baxter, Peter L. Bartlett
GLOBECOM
2009
IEEE
14 years 2 months ago
Semi-Blind Gradient-Newton CMA and SDD Algorithm for MIMO Space-Time Equalisation
— Semi-blind space-time equalisation is considered for dispersive multiple-input multiple-output systems that employ high-throughput quadrature amplitude modulation signalling. A...
S. Chen, Lajos Hanzo, H.-T. Cheng
GECCO
2004
Springer
122views Optimization» more  GECCO 2004»
14 years 1 months ago
Gradient-Based Learning Updates Improve XCS Performance in Multistep Problems
This paper introduces a gradient-based reward prediction update mechanism to the XCS classifier system as applied in neuralnetwork type learning and function approximation mechani...
Martin V. Butz, David E. Goldberg, Pier Luca Lanzi
ICIP
2000
IEEE
14 years 9 months ago
Curve Evolution, Boundary-Value Stochastic Processes, the Mumford-Shah Problem, and Missing Data Applications
We present an estimation-theoretic approach to curve evolution for the Mumford-Shah problem. By viewing an active contour as the set of discontinuities in the Mumford-Shah problem...
Andy Tsai, Anthony J. Yezzi, Alan S. Willsky
NIPS
2001
13 years 9 months ago
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning
Policy gradient methods for reinforcement learning avoid some of the undesirable properties of the value function approaches, such as policy degradation (Baxter and Bartlett, 2001...
Evan Greensmith, Peter L. Bartlett, Jonathan Baxte...