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ORCIDMachine Learning, Volume 22, 1996
Volume 22, Numbers 1-3, January 1996
- Thomas G. Dietterich:
Editorial. 5-6 - Leslie Pack Kaelbling:
Introduction. 7-9 - David E. Moriarty, Risto Miikkulainen:
Efficient Reinforcement Learning through Symbiotic Evolution. 11-32 - Steven J. Bradtke, Andrew G. Barto:
Linear Least-Squares Algorithms for Temporal Difference Learning. 33-57 - John N. Tsitsiklis, Benjamin Van Roy:
Feature-Based Methods for Large Scale Dynamic Programming. 59-94 - Robert E. Schapire, Manfred K. Warmuth:
On the Worst-Case Analysis of Temporal-Difference Learning Algorithms. 95-121 - Satinder P. Singh, Richard S. Sutton:
Reinforcement Learning with Replacing Eligibility Traces. 123-158 - Sridhar Mahadevan:
Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results. 159-195 - Matthias Heger:
The Loss from Imperfect Value Functions in Expectation-Based and Minimax-Based Tasks. 197-225 - Sven Koenig, Reid G. Simmons:
The Effect of Representation and Knowledge on Goal-Directed Exploration with Reinforcement-Learning Algorithms. 227-250 - Richard Maclin, Jude W. Shavlik:
Creating Advice-Taking Reinforcement Learners. 251-281 - Jing Peng, Ronald J. Williams:
Incremental Multi-Step Q-Learning. 283-290
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