Proceedings of the seventh international conference (1990) on Machine learning
Efficient learning and planning within the Dyna framework
Adaptive Behavior
Temporal difference learning and TD-Gammon
Communications of the ACM
Adaptive Behavior
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Learning to act using real-time dynamic programming
Artificial Intelligence
Learning polite behavior with situation models
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
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In this paper, we propose a new idea for tabular TD(驴) algorithm. In TD learning, rewards are propagated along the sequence of state/action pairs that have been visited recently. In complement to this, we propose to propagate rewards towards neighboring state/action pairs along this sequence, though unvisited. This leads to a great decrease in the number of iterations required for TD(驴) to be able to generalize since it is no longer necessary that a state/action pair is visited for its Q-value to be updated. The use of this propagation process makes tabular TD(驴) coming closer to neural net based TD(驴) with regards to its ability to generalize, while keeping unchanged other properties of tabular TD(驴).