Reinforcement learning with function approximation for cooperative navigation tasks

In this paper, we propose a reinforcement learning approach to address multi-robot cooperative navigation tasks in infinite settings. We propose an algorithm to simultaneously address the problems of learning and coordination in multi-robot problems. The proposed algorithm extends those existing in...

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Bibliographic Details
Main Authors: Melo, F.S., Ribeiro, M.I.
Format: Conference Proceeding
Language:English
Subjects:
Function approximation
Learning
Mobile robots
Navigation
Observability
Robot kinematics
Robot sensing systems
Robotics and automation
Testing
USA Councils
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Summary:In this paper, we propose a reinforcement learning approach to address multi-robot cooperative navigation tasks in infinite settings. We propose an algorithm to simultaneously address the problems of learning and coordination in multi-robot problems. The proposed algorithm extends those existing in the literature, allowing to address simultaneous learning and coordination in problems with an infinite state-space. We also present the results obtained in several test scenarios featuring multi-robot navigation situations with partial observability.
ISSN:1050-4729
2577-087X
DOI:10.1109/ROBOT.2008.4543717