Adaptive Representations for Reinforcement Learning

1. Front Matter

   PDF

2. Introduction

   • Shimon Whiteson

   Pages 1-5

3. Reinforcement Learning

   • Shimon Whiteson

   Pages 7-15

4. On-Line Evolutionary Computation

   • Shimon Whiteson

   Pages 17-30

5. Evolutionary Function Approximation

   • Shimon Whiteson

   Pages 31-46

6. Sample-Efficient Evolutionary Function Approximation

   • Shimon Whiteson

   Pages 47-52

7. Automatic Feature Selection for Reinforcement Learning

   • Shimon Whiteson

   Pages 53-64

8. Adaptive Tile Coding

   • Shimon Whiteson

   Pages 65-76

9. RelatedWork

   • Shimon Whiteson

   Pages 77-94

10. Conclusion

    • Shimon Whiteson

    Pages 95-104

11. Back Matter

    PDF

This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own representations have the potential to dramatically improve performance. This book introduces two novel approaches for automatically discovering high-performing representations. The first approach synthesizes temporal difference methods, the traditional approach to reinforcement learning, with evolutionary methods, which can learn representations for a broad class of optimization problems. This synthesis is accomplished by customizing evolutionary methods to the on-line nature of reinforcement learning and using them to evolve representations for value function approximators. The second approach automatically learns representations based on piecewise-constant approximations of value functions. It begins with coarse representations and gradually refines them during learning, analyzing the current policy and value function to deduce the best refinements. This book also introduces a novel method for devising input representations. This method addresses the feature selection problem by extending an algorithm that evolves the topology and weights of neural networks such that it evolves their inputs too. In addition to introducing these new methods, this book presents extensive empirical results in multiple domains demonstrating that these techniques can substantially improve performance over methods with manual representations.

• Instituut voor Informatica, Universiteit van Amsterdam, Amsterdam, Netherlands

  Shimon Whiteson

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