Learning to Behave by Environment Reinforcement

Authors:
Leonardo A. Scardua;Anna H. Reali Costa;Jose Jaime da Cruz
Affiliations:
-;-;-
Venue:
RoboCup-99: Robot Soccer World Cup III
Year:
2000

Citing 7
Cited 0

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Abstract

This paper describes a softbot agent capable of learning to choose its actions, in order to achieve its goal when facing an opponent in a dynamic environment. The agent uses rewards gathered from the environment to assess and improve the quality of its own behavior. A multilayer perceptron neural network is assessed regarding its adequacy as a value function approximator for state-action pairs in the robotic soccer domain.