Heterogeneous and Hierarchical Cooperative Learning via Combining Decision Trees
Decision trees, being human readable and hierarchically structured, provide a suitable mean to derive state-space abstraction and simplify the inclusion of the available knowledge for a reinforcement learning (RL) agent. In this paper, we address two approaches to combine and purify the available kn...
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Published in | 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 2684 - 2690 |
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Main Authors | , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.10.2006
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Abstract | Decision trees, being human readable and hierarchically structured, provide a suitable mean to derive state-space abstraction and simplify the inclusion of the available knowledge for a reinforcement learning (RL) agent. In this paper, we address two approaches to combine and purify the available knowledge in the abstraction trees, stored among different RL agents in a multi-agent system, or among the decision trees learned by the same agent using different methods. Simulation results in nondeterministic football learning task provide strong evidences for enhancement in convergence rate and policy performance |
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AbstractList | Decision trees, being human readable and hierarchically structured, provide a suitable mean to derive state-space abstraction and simplify the inclusion of the available knowledge for a reinforcement learning (RL) agent. In this paper, we address two approaches to combine and purify the available knowledge in the abstraction trees, stored among different RL agents in a multi-agent system, or among the decision trees learned by the same agent using different methods. Simulation results in nondeterministic football learning task provide strong evidences for enhancement in convergence rate and policy performance |
Author | Siegwart, R. Asadpour, M. Ahmadabadi, M.N. |
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Snippet | Decision trees, being human readable and hierarchically structured, provide a suitable mean to derive state-space abstraction and simplify the inclusion of the... |
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SubjectTerms | Control systems Decision trees Humans Intelligent agent Intelligent control Intelligent robots Intelligent structures Learning systems Multiagent systems Process control |
Title | Heterogeneous and Hierarchical Cooperative Learning via Combining Decision Trees |
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