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NIPS
2001
15 years 7 months ago
Model-Free Least-Squares Policy Iteration
We propose a new approach to reinforcement learning which combines least squares function approximation with policy iteration. Our method is model-free and completely off policy. ...
Michail G. Lagoudakis, Ronald Parr
ATAL
2009
Springer
16 years 26 days 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
GECCO
2006
Springer
185views Optimization» more  GECCO 2006»
15 years 10 months ago
Robot gaits evolved by combining genetic algorithms and binary hill climbing
In this paper an evolutionary algorithm is used for evolving gaits in a walking biped robot controller. The focus is fast learning in a real-time environment. An incremental appro...
Lena Mariann Garder, Mats Erling Høvin
ATAL
2009
Springer
15 years 4 months ago
Decentralized Learning in Wireless Sensor Networks
In this paper we use a reinforcement learning algorithm with the aim to increase the autonomous lifetime of a Wireless Sensor Network (WSN) and decrease latency in a decentralized...
Mihail Mihaylov, Karl Tuyls, Ann Nowé
EUROCAST
2007
Springer
182views Hardware» more  EUROCAST 2007»
16 years 13 days ago
A k-NN Based Perception Scheme for Reinforcement Learning
Abstract a paradigm of modern Machine Learning (ML) which uses rewards and punishments to guide the learning process. One of the central ideas of RL is learning by “direct-online...
José Antonio Martin H., Javier de Lope Asia...