Adaptive Representations for Reinforcement Learning

Presenting the main results of new algorithms for reinforcement learning, this book also introduces a novel method for devising input representations as well as presenting a way to find a minimal set of features sufficient to describe the agent's current state.

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.

Part 1 Introduction.- Part 2 Reinforcement Learning.- Part 3 On-Line Evolutionary Computation.- Part 4 Evolutionary Function Approximation.- Part 5 Sample-Efficient Evolutionary Function Approximation.- Part 6 Automatic Feature Selection for Reinforcement Learning.- Part 7 Adaptive Tile Coding.- Part 8 RelatedWork.- Part 9 Conclusion.- Part 10 Statistical Significance.