Game theoretic approaches to motion planning in robot soccer / Marcus Post. 2008
Content
- Introduction
- Reinforcement Learning (RL) and Game Theory
- Dynamical Systems and Markov Processes
- Markov Decision Processes (MDPs)
- Matrix Games
- Two Player Zero Sum Markov Games (2P-ZS-MGs)
- General Markov Games, Differential Games, and Advanced Concepts of RL
- Model Reduction and Symmetry
- Supervised Learning (SL), Function Approximation, Generalisation
- Introduction
- General Approximation Results
- Generalisation
- Function Approximation with Automated Basis Determination
- Value Iteration with SL: Convergence Result
- Combination of RL and SL: Practical Results from Literature
- Robot Soccer and Other Applications
- Modeling Robot Soccer
- Numerical Results of Grid Soccer
- Preliminaries for the Following Subsections
- Reasoning for 2P-ZS-MG Modelling: Comparison of MDP and 2P-ZS-MG strategies
- Relating Policies to Humanoid Soccer Characteristics
- Comparison of DP and RL Techniques
- Comparison of Different DP Techniques with Various Parameters
- Comparison of Standard Methods and SL Techniques
- Towards Multi-Player Robot Soccer: 2v2 Grid Soccer
- A New Algorithm: MaG-Clus-VI
- From Grid Soccer to Robot Soccer: Practical Issues
- Other Applications
- Conclusion and Outlook
- Basics of Group Homomorphisms and Group Actions
- Bellman Equations and Iterative Linear Solvers
- The Software Package DRPOST
- Detailed Tables of Numerical Results
- Initial Value Functions [0] and Discount Factors
- Additional Figures and Tables for the Comparative Studies of DP and RL methods
- List of Figures
- List of Tables
- Glossary
- Bibliography
- Index