Created by W.Langdon from gp-bibliography.bib Revision:1.2031
@InProceedings{Doucette:2011:RtAhtAeoEEPSuaEGST,
title = "Revisiting the Acrobot `height' task: An example of
Efficient Evolutionary Policy Search under an Episodic
Goal Seeking Task",
author = "John Doucette and Malcolm Heywood",
pages = "468--475",
booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary
Computation",
year = "2011",
editor = "Alice E. Smith",
month = "5-8 " # jun,
address = "New Orleans, USA",
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming, Adaptive
dynamic programming and reinforcement learning,
Coevolution and collective behaviour",
abstract = "Evolutionary methods for addressing the temporal
sequence learning problem generally fall into policy
search as opposed to value function optimisation
approaches. Various recent results have made the claim
that the policy search approach is at best inefficient
at solving episodic `goal seeking' tasks i.e., tasks
under which the reward is limited to describing
properties associated with a successful outcome have no
qualification for degrees of failure. This work
demonstrates that such a conclusion is due to a lack of
diversity in the training scenarios. We therefore
return to the Acrobot `height' task domain originally
used to demonstrate complete failure in evolutionary
policy search. This time a very simple stochastic
sampling heuristic for defining a population of
training configurations is introduced. Benchmarking two
recent evolutionary policy search algorithms -- Neural
Evolution of Augmented Topologies (NEAT) and Symbiotic
Bid-Based (SBB) Genetic Programming -- under this
condition demonstrates solutions as effective as those
returned by advanced value function methods. Moreover
this is achieved while remaining within the evaluation
limit imposed by the original study.",
notes = "CEC2011 sponsored by the IEEE Computational
Intelligence Society, and previously sponsored by the
EPS and the IET.",
}
Genetic Programming entries for John Doucette Malcolm Heywood