TD-Gammon, a self-teaching backgammon program, achieves master-level play
Neural Computation
Automatically generating abstractions for planning
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Dynamic Programming
Transfer Learning via Inter-Task Mappings for Temporal Difference Learning
The Journal of Machine Learning Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
General game learning using knowledge transfer
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
State abstraction discovery from irrelevant state variables
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Using advice to transfer knowledge acquired in one reinforcement learning task to another
ECML'05 Proceedings of the 16th European conference on Machine Learning
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In order to claim fully general intelligence in an autonomous agent, the ability to learn is one of the most central capabilities. Classical machine learning techniques have had many significant empirical successes, but large real-world problems that are of interest to generally intelligent agents require learning much faster (with much less training experience) than is currently possible. This paper presents transfer learning, where knowledge from a learned task can be used to significantly speed up learning in a novel task, as the key to achieving the learning capabilities necessary for general intelligence. In addition to motivating the need for transfer learning in an intelligent agent, we introduce a novel method for selecting types of tasks to be used for transfer and empirically demonstrate that such a selection can lead to significant increases in training speed in a two-player game.