Reinforcement function design and bias for efficient learning in mobile robots
- Oak Ridge National Lab., TN (United States). Computer Science and Mathematics Div.
- Univ. of Buenos Aires (Argentina). Dept. Computacion
The main paradigm in sub-symbolic learning robot domain is the reinforcement learning method. Various techniques have been developed to deal with the memorization/generalization problem, demonstrating the superior ability of artificial neural network implementations. In this paper, the authors address the issue of designing the reinforcement so as to optimize the exploration part of the learning. They also present and summarize works relative to the use of bias intended to achieve the effective synthesis of the desired behavior. Demonstrative experiments involving a self-organizing map implementation of the Q-learning and real mobile robots (Nomad 200 and Khepera) in a task of obstacle avoidance behavior synthesis are described. 3 figs., 5 tabs.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Office of Energy Research, Washington, DC (United States)
- DOE Contract Number:
- AC05-96OR22464
- OSTI ID:
- 661571
- Report Number(s):
- ORNL/CP-97926; CONF-980538-; ON: DE98003511; TRN: 98:006966
- Resource Relation:
- Conference: IEEE world congress on computational intelligence, Anchorage, AK (United States), 5-9 May 1998; Other Information: PBD: [1998]
- Country of Publication:
- United States
- Language:
- English
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