Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
The role of learning in autonomous robots
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Reinforcement learning architectures for animats
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Automatic programming of behavior-based robots using reinforcement learning
Artificial Intelligence
Technical Note: \cal Q-Learning
Machine Learning
Learning to act using real-time dynamic programming
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Reinforcement landmark learning
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Learning hierarchical control structures for multiple tasks and changing environments
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement Learning in the Multi-Robot Domain
Autonomous Robots
An Approach to Anytime Learning
ML '92 Proceedings of the Ninth International Workshop on Machine Learning
Practical Reinforcement Learning in Continuous Spaces
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Applications of the self-organising map to reinforcement learning
Neural Networks - New developments in self-organizing maps
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Distributed path planning for mobile robots using a swarm of interacting reinforcement learners
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Dimensionality effects on the Markov property in shape memory alloy hysteretic environment
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Learning Affordances of Consummatory Behaviors: Motivation-Driven Adaptive Perception
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Self-Organizing Sensorimotor Maps Plus Internal Motivations Yield Animal-Like Behavior
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Open-ended evolution as a means to self-organize heterogeneous multi-robot systems in real time
Robotics and Autonomous Systems
An adaptive robot motivational system
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Incremental skill acquisition for self-motivated learning animats
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Hedonic value: enhancing adaptation for motivated agents
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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This paper introduces an integration of reinforcement learning and behavior-based control designed to produce real-time learning in situated agents. The model layers a distributed and asynchronous reinforcement learning algorithm over a learned topological map and standard behavioral substrate to create a reinforcement learning complex. The topological map creates a small and task-relevant state space that aims to make learning feasible, while the distributed and asynchronous aspects of the architecture make it compatible with behavior-based design principles.We present the design, implementation and results of an experiment that requires a mobile robot to perform puck foraging in three artificial arenas using the new model, random decision making, and layered standard reinforcement learning. The results show that our model is able to learn rapidly on a real robot in a real environment, learning and adapting to change more quickly than both alternatives. We show that the robot is able to make the best choices it can given its drives and experiences using only local decisions and therefore displays planning behavior without the use of classical planning techniques.