1. Introduction

   • Satinder Singh

   Introduction Pages: 107 - 109

2. Reinforcement Learning for Call Admission Control and Routing under Quality of Service Constraints in Multimedia Networks

   • Hui Tong
   • Timothy X Brown

   OriginalPaper Pages: 111 - 139

3. Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts

   • Amy McGovern
   • Eliot Moss
   • Andrew G. Barto

   OriginalPaper Pages: 141 - 160

4. Kernel-Based Reinforcement Learning

   • Dirk Ormoneit
   • Śaunak Sen

   OriginalPaper Pages: 161 - 178

5. On Average Versus Discounted Reward Temporal-Difference Learning

   • John N. Tsitsiklis
   • Benjamin Van Roy

   OriginalPaper Pages: 179 - 191

6. A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes

   • Michael Kearns
   • Yishay Mansour
   • Andrew Y. Ng

   OriginalPaper Pages: 193 - 208

7. Near-Optimal Reinforcement Learning in Polynomial Time

   • Michael Kearns
   • Satinder Singh

   OriginalPaper Pages: 209 - 232

8. Technical Update: Least-Squares Temporal Difference Learning

   • Justin A. Boyan

   OriginalPaper Pages: 233 - 246

9. Continuous-Action Q-Learning

   • José del R. Millán
   • Daniele Posenato
   • Eric Dedieu

   OriginalPaper Pages: 247 - 265

10. Risk-Sensitive Reinforcement Learning

    • Oliver Mihatsch
    • Ralph Neuneier

    OriginalPaper Pages: 267 - 290

11. Variable Resolution Discretization in Optimal Control

    • Rémi Munos
    • Andrew Moore

    OriginalPaper Pages: 291 - 323

12. Structure in the Space of Value Functions

    • David Foster
    • Peter Dayan

    OriginalPaper Pages: 325 - 346