Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Generalization and scaling in reinforcement learning
Advances in neural information processing systems 2
Practical Issues in Temporal Difference Learning
Machine Learning
Technical Note: \cal Q-Learning
Machine Learning
Reinforcement learning for robots using neural networks
Reinforcement learning for robots using neural networks
TD-Gammon, a self-teaching backgammon program, achieves master-level play
Neural Computation
Incremental multi-step Q-learning
Machine Learning - Special issue on reinforcement learning
Neural networks: a systematic introduction
Neural networks: a systematic introduction
Self-organizing maps
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Kohonen Maps
Dynamic Programming
Temporal credit assignment in reinforcement learning
Temporal credit assignment in reinforcement learning
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
An Architecture for Behavior-Based Reinforcement Learning
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Actor-Critic Models of Reinforcement Learning in the Basal Ganglia: From Natural to Artificial Rats
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Evolutionary Function Approximation for Reinforcement Learning
The Journal of Machine Learning Research
Postural Control of Two-Stage Inverted Pendulum Using Reinforcement Learning and Self-organizing Map
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
A reinforcement learning model for supply chain ordering management: An application to the beer game
Decision Support Systems
Dynamic packaging in e-retailing with stochastic demand over finite horizons: A Q-learning approach
Expert Systems with Applications: An International Journal
Assigning discounts in a marketing campaign by using reinforcement learning and neural networks
Expert Systems with Applications: An International Journal
A method of stepwise benchmarking for inefficient DMUs based on the proximity-based target selection
Expert Systems with Applications: An International Journal
Direct code access in self-organizing neural networks for reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A motor learning neural model based on Bayesian network and reinforcement learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Goal-directed feature learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Encoding robotic sensor states for Q-learning using the self-organizing map
Journal of Computing Sciences in Colleges
Extending context spaces theory by proactive adaptation
ruSMART/NEW2AN'10 Proceedings of the Third conference on Smart Spaces and next generation wired, and 10th international conference on Wireless networking
Grey reinforcement learning for incomplete information processing
TAMC'06 Proceedings of the Third international conference on Theory and Applications of Models of Computation
Q learning based on self-organizing fuzzy radial basis function network
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Self-Organizing reinforcement learning model
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
Reduction of state space in reinforcement learning by sensor selection
Artificial Life and Robotics
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This article is concerned with the representation and generalisation of continuous action spaces in reinforcement learning (RL) problems. A model is proposed based on the self-organising map (SOM) of Kohonen [Self Organisation and Associative Memory, 1987] which allows either the one-to-one, many-to-one or one-to-many structure of the desired state-action mapping to be captured. Although presented here for tasks involving immediate reward, the approach is easily extended to delayed reward. We conclude that the SOM is a useful tool for providing real-time, on-line generalisation in RL problems in which the latent dimensionalities of the state and action spaces are small. Scalability issues are also discussed.