The Effect of Reinforcement Signal Error in Reinforcement Learning

TITLE: The Effect of Reinforcement Signal Error in Reinforcement Learning (Translated)

ABSTRACT: Designing reinforcement signal is fundamental problem in reinforcement learning. Intelligent agent’s designer can guide the learner agent to its desired behavior by selecting an appropriate reinforcement signal. However, there is no general methodology for designing that signal and the designed signal is different from the unknown ideal one in many cases. In this paper, this difference is considered as a bounded-norm error in reinforcement signal and its effects on the value function and the policy of the agent is calculated as some upper bounds. In the end, the mathematical results are tested in an experiment. (Translated)

This is my The Computer Society of Iran Computer Conference (CSICC) 2005 paper. As it is written in Persian, I translate its abstract and put it in this weblog. It is probable that I revise it and send it to an international conference or even journal.

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