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ATAL
2009
Springer
15 years 10 months ago
An empirical analysis of value function-based and policy search reinforcement learning
In several agent-oriented scenarios in the real world, an autonomous agent that is situated in an unknown environment must learn through a process of trial and error to take actio...
Shivaram Kalyanakrishnan, Peter Stone
134
Voted
ICRA
2008
IEEE
206views Robotics» more  ICRA 2008»
15 years 10 months ago
Memory-based learning for visual odometry
Abstract— We present and examine a technique for estimating the ego-motion of a mobile robot using memory-based learning and a monocular camera. Unlike other approaches that rely...
Richard Roberts, Hai Nguyen, Niyant Krishnamurthi,...
130
Voted
ICML
1999
IEEE
15 years 7 months ago
Learning Hierarchical Performance Knowledge by Observation
Developing automated agents that intelligently perform complex real world tasks is time consuming and expensive. The most expensive part of developing these intelligent task perfo...
Michael van Lent, John E. Laird
132
Voted
IWANN
1999
Springer
15 years 7 months ago
Using Temporal Neighborhoods to Adapt Function Approximators in Reinforcement Learning
To avoid the curse of dimensionality, function approximators are used in reinforcement learning to learn value functions for individual states. In order to make better use of comp...
R. Matthew Kretchmar, Charles W. Anderson
150
Voted
ATAL
2010
Springer
15 years 3 months ago
Inter-robot transfer learning for perceptual classification
We introduce the novel problem of inter-robot transfer learning for perceptual classification of objects, where multiple heterogeneous robots communicate and transfer learned obje...
Zsolt Kira