Direct Policy Search Reinforcement Learning for Robot Control

Authors:
Andres El-Fakdi;Marc Carreras;Narcís Palomeras
Affiliations:
University of Girona, Spain;University of Girona, Spain;University of Girona, Spain
Venue:
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Year:
2005

Citing 6
Cited 1

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Neuro-Dynamic Programming

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Reinforcement Learning in POMDP's via Direct Gradient Ascent

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On Actor-Critic Algorithms

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Neural Networks: A Comprehensive Foundation (3rd Edition)

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Adaptive navigation for autonomous robots

Robotics and Autonomous Systems

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Abstract

In this paper, we present Policy Methods as an alternative to Value Methods to solve Reinforcement Learning problems. The paper proposes a Direct Policy Search algorithm that uses a Neural Network to represent the control policies. Details about the algorithm and the update rules are given. The main application of the proposed algorithm is to implement robot control systems, in which the generalization problem usually arises. In this paper, we point out the suitability of our algorithm in a RL benchmark, that was specially designed to test the generalization capability of RL algorithms. Results check out that policy methods obtain better results than value methods in these situations.