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Digital Library of the
European Council for Modelling and Simulation |
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Title: |
Learning
Of Autonomous Agent In Virtual Environment |
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Authors: |
Pavel Nahodil, Jaroslav Vítků |
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Published in:
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(2012).ECMS
2012 Proceedings edited by: K. G. Troitzsch, M. Moehring, U. Lotzmann.
European Council for Modeling and Simulation. doi:10.7148/2012 ISBN:
978-0-9564944-4-3 26th
European Conference on Modelling and Simulation, Shaping reality through simulation Koblenz,
Germany, May 29 – June 1 2012 |
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Citation
format: |
Nahodil, P., & Vitku, J.
(2012). Learning Of Autonomous Agent In Virtual Environment. ECMS 2012
Proceedings edited by: K. G. Troitzsch, M. Moehring, U.
Lotzmann (pp. 373-379). European Council for Modeling and Simulation. doi:10.7148/2012-0373-0379 |
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DOI: |
http://dx.doi.org/10.7148/2012-0373-0379 |
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Abstract: |
Presented
topic is from area of development of artificial creatures and proposes new
architecture of autonomous agent. The work builds on a research of the latest
approaches to Artificial Life, realized by the Department of Cybernetics, CTU
in Prague in the last twenty years. This architecture design combines
knowledge from Artificial Intelligence (AI), Ethology, Artificial
Life (ALife) and Intelligent Robotics. From the field of classical AI,
the fusion of reinforcement learning, planning and artificial neural network
into one more complex control system was used here. The main principle
of its function is inspired by the field of Ethology, this means that life of
given agent tries to be similar to life of an animal in the Nature, where
animal learns relatively autonomously from simpler principles towards the
more complex ones. The
architecture supports on-line learning of all knowledge from the scratch, while
the core principle is in hierarchical Reinforcement Learning (RL),
this action hierarchy is created autonomously based solely on agents
interaction with an environment. The main key idea behind this approach is in
original implementation of a domain independent hierarchical planner. Our
planner is able to operate with behaviors learned by the RL. It means that an
autonomously gained hierarchy of actions can be used not only by action
selection mechanisms based on the reinforcement learning, but also by a
planning system. This gives the agent ability to utilize high-level
deliberative problem solving based solely on his experiences. In order to
deal with higher-level control rather than a sensory system, the life of
agent was simulated in a virtual environment. |
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