| Autor: |
Khosla, Rajiv, Howlett, Robert J., Jain, Lakhmi C., Kitakoshi, Daisuke, Shioya, Hiroyuki, Nakano, Ryohei |
| Zdroj: |
Knowledge-Based Intelligent Information & Engineering Systems (9783540288978); 2005, p730-737, 8p |
| Abstrakt: |
We have proposed an online policy-improving system of reinforcement learning (RL) agents with a mixture model of Bayesian Networks (BNs), and discussed properties of the system. In this paper, two types of mixture models have been applied to the system. A structure of BN in the mixture model is selected based on data collected by agents in an environment, and is regarded as a stochastic knowledge of the environment. This research investigates the adaptability of our system to dynamic environments containing an unexperienced environment, in which an agent does not have the knowledge. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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