A teaching method for reinforcement learning
ML92 Proceedings of the ninth international workshop on Machine learning
Incorporating advice into agents that learn from reinforcements
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Creating advice-taking reinforcement learners
Machine Learning - Special issue on reinforcement learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Robot Learning From Demonstration
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Exploration and apprenticeship learning in reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Supervised Reinforcement Learning Using Behavior Models
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
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We introduce a Supervised Reinforcement Learning (SRL) algorithm for autonomous learning problems where an agent is required to deal with high dimensional spaces. In our learning algorithm, behavior models learned from a set of examples, are used to dynamically reduce the set of relevant actions at each state of the environment encountered by the agent. Such subsets of actions are used to guide the agent through promising parts of the action space, avoiding the selection of useless actions. The algorithm handles continuous states and actions. Our experimental work with a difficult robot learning task shows clearly how this approach can significantly speed up the learning process and improve the final performance.