1. Adaptive Reinforcement Learning Agents in RTS Games Eric Kok

2. 1.Introduction

3. Abstract

4. Motivation

5. RTS Games

6. Bos Wars

7. Agent Technology

8. Reinforcement Learning

9. 2.Existing Research

10. Dynamic Scripting

11. Evolutionary Learning

12. Case-based Plan selection

13. Concurrent hierarchal Reinforcement Learning

14. 3.Learning winning RTS Game Strategies

15. Learning in 2apl agents

16. BDI Agents

17. Temporal Difference Learning

18. Monte-Carlo Methods

19. 4.Improvements implemented

20. Rule Guards

21. Softmax Exploration Policy

22. Strategy Hierarchy

23. MC-DS Hybrid

24. 5.Improvements out of project scope

25. Dynamic Scripting Enhancement

26. Comparison of more Learning algorithms

27. Game State Function Approximation

28. Semi-MDP Reinforcement Learning

29. 6.Adaptation to opponent Strategies

30. Implicit Adaptation

31. Explicit Adaptation

32. Multi-Agent Learning

33. 8.Adaption Ideas Out of scope

34. Explicit Adaptation through player modeling

35. Opponent Strength Adaptation

36. 9.Project Conclusions

37. Fail Rates

38. Av. Turning Points

39. Usability of Learning agents in computer games

40. 10.Future Research

41. Learning on a full complex game task

42. Strategy Visualization Tool

43. Explicit Player Modeling