1. Editorial

   • Thomas G. Dietterich

   Editorial Board Pages: 5 - 6

2. Editorial

   EditorialNotes Pages: 5 - 6

3. Introduction

   • Leslie Pack Kaelbling

   Legacy Pages: 7 - 9

4. Introduction

   • Leslie Pack Kaelbling

   EditorialNotes Pages: 7 - 9

5. Efficient Reinforcement Learning through Symbiotic Evolution

   • David E. Moriarty
   • Risto Mikkulainen

   OriginalPaper Pages: 11 - 32

6. Efficient reinforcement learning through symbiotic evolution

   • David E. Moriarty
   • Risto Miikkulainen

   OriginalPaper Pages: 11 - 32

7. Linear Least-Squares Algorithms for Temporal Difference Learning

   • Steven J. Bradtke
   • Andrew G. Barto

   OriginalPaper Pages: 33 - 57

8. Linear Least-Squares algorithms for temporal difference learning

   • Steven J. Bradtke
   • Andrew G. Barto

   OriginalPaper Pages: 33 - 57

9. Feature-Based Methods for Large Scale Dynamic Programming

   • John N. Tsitsiklis
   • Benjamin Van Roy

   OriginalPaper Pages: 59 - 94

10. Feature-based methods for large scale dynamic programming

    • John N. Tsitsiklis
    • Benjamin van Roy

    OriginalPaper Pages: 59 - 94

11. On the Worst-Case Analysis of Temporal-Difference Learning Algorithms

    • Robert E. Schapire
    • Manfred K. Warmuth

    OriginalPaper Pages: 95 - 121

12. On the worst-case analysis of temporal-difference learning algorithms

    • Robert E. Schapire
    • Manfred K. Warmuth

    OriginalPaper Pages: 95 - 121

13. Reinforcement Learning with Replacing Eligibility Traces

    • Satinder P. Singh
    • Richard S. Sutton

    OriginalPaper Pages: 123 - 158

14. Reinforcement learning with replacing eligibility traces

    • Satinder P. Singh
    • Richard S. Sutton

    OriginalPaper Pages: 123 - 158

15. Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results

    • Sridhar Mahadevan

    OriginalPaper Pages: 159 - 195

16. Average reward reinforcement learning: Foundations, algorithms, and empirical results

    • Sridhar Mahadevan

    OriginalPaper Pages: 159 - 195

17. The Loss from Imperfect Value Functions in Expectation-Based and Minimax-Based Tasks

    • Matthias Heger

    OriginalPaper Pages: 197 - 225

18. The loss from imperfect value functions in expectation-based and minimax-based tasks

    • Matthias Heger

    OriginalPaper Pages: 197 - 225

19. The Effect of Representation and Knowledge on Goal-Directed Exploration with Reinforcement-Learning Algorithms

    • Sven Koenig
    • Reid G. Simmons

    OriginalPaper Pages: 227 - 250

20. The effect of representation and knowledge on goal-directed exploration with reinforcement-learning algorithms

    • Sven Koenig
    • Reid G. Simmons

    OriginalPaper Pages: 227 - 250

21. Creating Advice-Taking Reinforcement Learners

    • Richard Maclin
    • Jude W. Shavlik

    OriginalPaper Pages: 251 - 281

22. Creating advice-taking reinforcement learners

    • Richard Maclin
    • Jude W. Shavlik

    OriginalPaper Pages: 251 - 281

23. Incremental Multi-Step Q-Learning

    • Jing Peng
    • Ronald J. Williams

    OriginalPaper Pages: 283 - 290

24. Incremental multi-step Q-learning

    • Jing Peng
    • Ronald J. Williams

    Technical Note Pages: 283 - 290