Technical Note: \cal Q-Learning
Machine Learning
Swarm intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Distributed algorithms for guiding navigation across a sensor network
Proceedings of the 9th annual international conference on Mobile computing and networking
Performance aware tasking for environmentally powered sensor networks
Proceedings of the joint international conference on Measurement and modeling of computer systems
An Architecture for Behavior-Based Reinforcement Learning
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Cooperative self-organization in a heterogeneous swarm robotic system
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Cooperative stigmergic navigation in a heterogeneous robotic swarm
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
ACM Transactions on Applied Perception (TAP)
Evolutionary dynamics of ant colony optimization
MATES'12 Proceedings of the 10th German conference on Multiagent System Technologies
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Path planning for mobile robots in stochastic, dynamic environments is a difficult problem and the subject of much research in the field of robotics. While many approaches to solving this problem put the computational burden of path planning on the robot, physical path planning methods place this burden on a set of sensor nodes distributed throughout the environment that can communicate information to each other about path costs. Previous approaches to physical path planning have looked at the performance of such networks in regular environments (e.g., office buildings) using highly structured, uniform deployments of networks (e.g., grids). Additionally, these networks do not make use of real experience obtained from the robots they assist in guiding. We extend previous work in this area by incorporating reinforcement learning techniques into these methods and show improved performance in simulated, rough terrain environments. We also show that these networks, which we term SWIRLs (Swarms of Interacting Reinforcement Learners), can perform well with deployment distributions that are not as highly structured as in previous approaches.