Automatic programming of behavior-based robots using reinforcement learning
Artificial Intelligence
Technical Note: \cal Q-Learning
Machine Learning
Reinforcement learning with replacing eligibility traces
Machine Learning - Special issue on reinforcement learning
Purposive behavior acquisition for a real robot by vision-based reinforcement learning
Machine Learning - Special issue on robot learning
Engineering and Scientific Computing with Scilab
Engineering and Scientific Computing with Scilab
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Machine Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Practical Reinforcement Learning in Continuous Spaces
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Continuous-Time Hierarchical Reinforcement Learning
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Emergent Specialization in Swarm Systems
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
A Knowledge-Level Approach for Building Human-Machine Cooperative Environment
CRW '98 Proceedings of the First International Workshop on Collective Robotics
Reinforcement learning of coordination in cooperative multi-agent systems
Eighteenth national conference on Artificial intelligence
Coordination in multiagent reinforcement learning: a Bayesian approach
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Calibrating pan-tilt cameras in wide-area surveillance networks
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Coordinating Multiple Agents via Reinforcement Learning
Autonomous Agents and Multi-Agent Systems
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Multiple reinforcement learning agents in a static environment
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
A convenient multicamera self-calibration for virtual environments
Presence: Teleoperators and Virtual Environments
Distributed vision system: a perceptual information infrastructure for robot navigation
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Rapid, safe, and incremental learning of navigation strategies
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper an omnidirectional Distributed Vision System (DVS) is presented. The presented DVS is able to learn to navigate a mobile robot in its working environment without any prior knowledge about calibration parameters of the cameras or the control law of the robot (this is an important feature if we want to apply this system to existing camera networks). The DVS consists of different Vision Agents (VAs) implemented by omnidirectional cameras. The main contribution of the work is the explicit distribution of the acquired knowledge in the DVS. The aim is to develop a totally autonomous system able not only to learn control policies by on-line learning, but also to deal with a changing environment and to improve its performance during lifetime. Once an initial knowledge is acquired by one Vision Agent, this knowledge can be transferred to other Vision Agents in order to exploit what was already learned. In this paper, first we investigate how the Vision Agent learns the knowledge, then we evaluate its performance and test the knowledge propagation on three different VAs. Experiments are reported both using a system simulator and using a prototype of the Distributed Vision System in a real environment demonstrating the feasibility of the approach.