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
Copyright © Wed Nov 11 23:41:29 2009
by Michael Ley (ley@uni-trier.de)
