원문정보
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초록
영어
The design of intelligent agents by means of reinforcement learning is studied in this paper. A relational reinforcement learning algorithm is used to achieve a compact knowledge representation.
Moreover, this approach allows to improve the learning performance by augmenting the algorithm with the so-called background knowledge. A case study on simulated physical robotic agents is performed and compared with our previous evolutionary robotics experiments in order to justify our approach.
목차
Abstract
1 Introduction
2 Reinforcement Learning Problem
3 Relational Reinforcement Learning
4 Experiments
4.1 Basic setting
4.2 The reward function
4.3 Results
5 Conclusion
References
1 Introduction
2 Reinforcement Learning Problem
3 Relational Reinforcement Learning
4 Experiments
4.1 Basic setting
4.2 The reward function
4.3 Results
5 Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보