Deep reinforcement learning : Starcraft learning environment by Gema Parreño at Big Data Spain 2017

A theorical description of reinforcement learning principles and a deep dive into DeepMind Research environment .

https://www.bigdataspain.org/2017/talk/reinforced-learning-deepmind-starcraft-learning-environment

Big Data Spain 2017
November 16th - 17th Kinépolis Madrid

Transcript

2. sTARcRAFT ii Deep Reinforced Learning Gema Parreño Piqueras @SoyGema
3. Supervised Learning
4. LEARNING by known data
5. Unsupervised Learning
6. LEARNING by unknown data
7. Reinforce Learning
8. Classify Machine Learning by learning method
9. Reinforce Learning LEARNING WHILE INTERACTING WITH ENVIRONMENT
10. States / Actions
11. ENVIRONMENT
12. Agent
13. Policy
14. States / Actions
15. The ATARI CASE
16. Environment description x 2600
17. Policy Architecture
18. Policy Architecture Input SPATIAL/ NON SPATIAL FEATURES
19. Policy Architecture Output SPATIAL/ NON SPATIAL ACTION POLICY
20. Results
21. Conclusion Meta solution
22. Challenges
23. TRIAL AND ERROR
24. EXPLORATION VS EXPLOTATION
25. STARCRAFT 2
26. What is sc2 and why?
27. Learning Environmnet
28. Policy Search
29. Agent Architectures
30. Mini games
31. Mini-game definition
32. Agent without training Agent trained
34. Sentry unit
35. Designing environment
36. The Maps 1st Iteration
37. The Maps 2nd Iteration
38. The Maps 3th Iteration
39. Results Results
40. Diferent type of Nets On random agent
41. Diferent type of Nets On scripted agent
42. The future
43. RL out of Videogames
44. The environment The environment
45. The GOAL The GOAL
46. The policy The Policy
47. Thanks! Deep Reinforced Learning Gema Parreño Piqueras @SoyGema