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Relocatable Action Models for Autonomous Navigation
Bethany R. Leer,Michael L. Littman +1 more
- 01 Jan 2007
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References
Learning to Predict by the Methods of Temporal Differences
TL;DR: This article introduces a class of incremental learning procedures specialized for prediction – that is, for using past experience with an incompletely known system to predict its future behavior – and proves their convergence and optimality for special cases and relation to supervised-learning methods.
Temporal Difference Learning and TD-Gammon
TL;DR: TD-GAMMON is a neural network that trains itself to be an evaluation function for the game of backgammon by playing against itself and learning from the outcome.
1.6K
Temporal difference learning and TD-Gammon
TL;DR: The domain of complex board games such as Go, chess, checkers, Othello, and backgammon has been widely regarded as an ideal testing ground for exploring a variety of concepts and approaches in artificial intelligence and machine learning.
R-max - a general polynomial time algorithm for near-optimal reinforcement learning
TL;DR: R-MAX is a very simple model-based reinforcement learning algorithm which can attain near-optimal average reward in polynomial time and formally justifies the ``optimism under uncertainty'' bias used in many RL algorithms.
•Proceedings Article
R-MAX: a general polynomial time algorithm for near-optimal reinforcement learning
Ronen I. Brafman,Moshe Tennenholtz +1 more
- 04 Aug 2001
TL;DR: R-MAX as mentioned in this paper is a model-based reinforcement learning algorithm which can attain near-optimal average reward in polynomial time, where the agent always maintains a complete, but possibly inaccurate model of its environment and acts based on the optimal policy derived from this model.
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