The document discusses Project Malmo, an open-source platform for AI experimentation based on Minecraft, aimed at facilitating research in areas like reinforcement learning, computer vision, and robotics. It details various reinforcement learning concepts, specifically Q-learning and the related parameters used for training agents in a simulated environment. Examples of Q-learning applications in the context of Minecraft are provided to illustrate the methodology.

1. #MinecraftRL @choas
Minecraft and Reinforcement Learning
Lars Gregori
@choas
labs.hybris.com

2. #MinecraftRL @choas
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3. Minecraft

4. #MinecraftRL @choas
Minecraft
Markus "Notch" Persson
Mojang AB
Best-selling PC game of all time
Exploration
Resource gathering
Crafting
Combat
Sandbox construction game
Creative + building aspects
Three-dimensional environment

5. Project Malmo

6. #MinecraftRL @choas
Project Malmo
Open Source (Github)
Microsoft Research Lab
Based on
Minecraft / Minecraft Forge
Agents written in
Python, Lua, C++, C#,
Java, Torch, ALE*
Mission XML
WorldState
Send Command
*Arcade Learning Environment

7. #MinecraftRL @choas
“The Project Malmo platform is designed to
support a wide range of experimentation
needs and can support research in robotics,
computer vision, reinforcement learning,
planning, multi-agent systems, and related
areas”The Malmo Platform for Artificial Intelligence Experimentation. Proc. 25th International Joint Conference on Artificial Intelligence
Project Malmo

8. Reinforcement Learning

9. #MinecraftRL @choas
Reinforcement Learning
Supervised
Learning
Unsupervised
Learning
Reinforcement
Learning

10. #MinecraftRL @choas
Reinforcement Learning
Observation Reward Action
Environment
Agent

11. #MinecraftRL @choas
“Reinforcement learning is like
trial-and-error learning.”David Silver
Reinforcement Learning

12. #MinecraftRL @choas
Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto
(1998)
Reinforcement Learning
Cliff Walking Example
Reward:
-1 per move
100 blue field
-100 lava field

13. #MinecraftRL @choas
Reinforcement Learning Demo

14. #MinecraftRL @choas
Q-Learning

15. #MinecraftRL @choas
Q-Learning

16. #MinecraftRL @choas
Q-Learning
ALPHA = 1.0 ### step-size parameter
GAMMA = 0.8 ### discount-rate parameter
old_q = q_table[prev_state][prev_action]
max_q = max(q_table[current_state][:])
new_q = old_q + ALPHA * (reward + GAMMA * max_q - old_q)

17. #MinecraftRL @choas
Q-Learning

18. #MinecraftRL @choas
Q-Learning
ALPHA = 1.0 ### step-size parameter
GAMMA = 0.8 ### discount-rate parameter
old_q = q_table[prev_state][prev_action]
max_q = max(q_table[current_state][:])
new_q = old_q + ALPHA * (reward + GAMMA * max_q - old_q)

19. #MinecraftRL @choas
Q-Learning
ALPHA = 1.0 ### step-size parameter
GAMMA = 0.8 ### discount-rate parameter
old_q = 0.0
max_q = max(q_table[current_state][:])
new_q = old_q + ALPHA * (reward + GAMMA * max_q - old_q)

20. #MinecraftRL @choas
Q-Learning
ALPHA = 1.0 ### step-size parameter
GAMMA = 0.8 ### discount-rate parameter
old_q = 0.0
max_q = 0.0
new_q = old_q + ALPHA * (reward + GAMMA * max_q - old_q)

21. #MinecraftRL @choas
Q-Learning
100
-1
ALPHA = 1.0 ### step-size parameter
GAMMA = 0.8 ### discount-rate parameter
old_q = 0.0
max_q = 0.0
new_q = old_q + ALPHA * (99.0 + GAMMA * max_q - old_q)

22. #MinecraftRL @choas
Q-Learning
100
-1
ALPHA = 1.0 ### step-size parameter
GAMMA = 0.8 ### discount-rate parameter
old_q = 0.0
max_q = 0.0
new_q = 0.0 + 1.0 * (99.0 + 0.8 * 0.0 - 0.0)

23. #MinecraftRL @choas
Q-Learning
100
-1
ALPHA = 1.0 ### step-size parameter
GAMMA = 0.8 ### discount-rate parameter
old_q = 0.0
max_q = 0.0
new_q = 0.0 + 1.0 * (99.0 + 0.8 * 0.0 - 0.0)

24. #MinecraftRL @choas
Q-Learning
100
-1
99.0
ALPHA = 1.0 ### step-size parameter
GAMMA = 0.8 ### discount-rate parameter
old_q = 0.0
max_q = 0.0
new_q = 99.0

25. #MinecraftRL @choas
Q-Learning
100
-1
99.0

26. #MinecraftRL @choas
Q-Learning
100
-1
99.0
ALPHA = 1.0
GAMMA = 0.8
old_q = q_table[prev_state][prev_action]
max_q = max(q_table[current_state][:])
new_q = old_q + ALPHA * (reward + GAMMA * max_q - old_q)

27. #MinecraftRL @choas
Q-Learning
100
-1
99.0
ALPHA = 1.0
GAMMA = 0.8
old_q = -1.0
max_q = max(q_table[current_state][:])
new_q = old_q + ALPHA * (reward + GAMMA * max_q - old_q)

28. #MinecraftRL @choas
Q-Learning
100
-1
99.0
ALPHA = 1.0
GAMMA = 0.8
old_q = -1.0
max_q = 99.0
new_q = old_q + ALPHA * (reward + GAMMA * max_q - old_q)

29. #MinecraftRL @choas
Q-Learning
100
-1
99.0
ALPHA = 1.0
GAMMA = 0.8
old_q = -1.0
max_q = 99.0
new_q = old_q + ALPHA * (-1.0 + GAMMA * max_q - old_q)

30. #MinecraftRL @choas
Q-Learning
100
-1
99.0
ALPHA = 1.0
GAMMA = 0.8
old_q = -1.0
max_q = 99.0
new_q = old_q + ALPHA * (-1.0 + 0.8 * 99.0 - old_q)

31. #MinecraftRL @choas
Q-Learning
100
-1
99.0
ALPHA = 1.0
GAMMA = 0.8
old_q = -1.0
max_q = 99.0
new_q = old_q + ALPHA * (-1.0 + 79.2 - old_q)

32. #MinecraftRL @choas
Q-Learning
100
-1
99.0
ALPHA = 1.0
GAMMA = 0.8
old_q = -1.0
max_q = 99.0
new_q = -1.0 + 1.0 * (-1.0 + 79.2 - -1.0)

33. #MinecraftRL @choas
Q-Learning
100
-1
99.0
ALPHA = 1.0
GAMMA = 0.8
old_q = -1.0
max_q = 99.0
new_q = -1.0 + 1.0 * (-1.0 + 79.2 + 1.0)

34. #MinecraftRL @choas
Q-Learning
100
-1
99.0
78.2
ALPHA = 1.0
GAMMA = 0.8
old_q = -1.0
max_q = 99.0
new_q = 78.2

35. #MinecraftRL @choas
[99 0 0 0] [ 0 -1 -1 0] [ 0 0 L 0]
[ L -1 -1 -1] [-1 -1 -1 -1] [-1 0 0 0]
[ L -1 -1 -1] [-1 -1 -1 -1] [-1 L 0 0]
[ L L -2 -1] [-2 -2 L -1]
[ L -2 -2 -2] [-2 -2 L L]
[ L -3 -2 L] [-2 -3 -2 -2] [-2 -3 L -2]
[ L L -3 L] [-3 L -3 -3] [-3 L -3 -3] [-2 L L -3]
Q Table
L = Lava
[ ← ↓ → ↑ ]

36. #MinecraftRL @choas
[99 0 0 0] [ 0 -1 -1 0] [ 0 0 L 0]
[ L -1 -1 78] [-1 -1 -1 -1] [-1 0 0 0]
[ L -1 -1 -1] [-1 -1 -1 -1] [-1 L 0 0]
[ L L -2 -1] [-2 -2 L -1]
[ L -2 -2 -2] [-2 -2 L L]
[ L -3 -2 L] [-2 -3 -2 -2] [-2 -3 L -2]
[ L L -3 L] [-3 L -3 -3] [-3 L -3 -3] [-2 L L -3]
Q Table
L = Lava
[ ← ↓ → ↑ ]

37. #MinecraftRL @choas
[99 0 0 0] [ 0 -1 -1 0] [ 0 0 L -1]
[ L -1 -1 78] [61 -1 -1 -1] [-1 -1 L -1]
[ L -2 -2 61] [-2 -1 -1 -1] [-1 L L -1]
[ L L -2 -2] [-2 -3 L -2]
[ L -2 -3 -2] [-3 -2 L L]
[ L -3 -3 L] [-3 -3 -3 -3] [-2 -3 L -3]
[ L L -3 L] [-3 L -3 -3] [-3 L -3 -3] [-3 L L -3]
Q Table
L = Lava
[ ← ↓ → ↑ ]

38. #MinecraftRL @choas
[99 0 0 0] [ 0 -1 -1 0] [ 0 0 L -1]
[ L -1 -1 78] [61 -1 -1 -1] [-1 -1 L -1]
[ L -2 -2 61] [-2 -1 -1 -1] [-1 L L -1]
[ L L -2 48] [-2 -3 L -2]
[ L -2 -3 -2] [-3 -2 L L]
[ L -3 -3 L] [-3 -3 -3 -3] [-3 -3 L -3]
[ L L -3 L] [-3 L -3 -3] [-3 L -3 -3] [-3 L L -3]
Q Table
L = Lava
[ ← ↓ → ↑ ]

39. #MinecraftRL @choas
[99 0 0 0] [78 -1 -1 0] [-1 -1 L -1]
[ L -1 -1 78] [61 -1 -1 -1] [48 -1 L -1]
[ L -2 -2 61] [-2 -2 -2 48] [-1 L L -1]
[ L L -2 48] [-2 -3 L 37]
[ L -3 -3 -2] [-3 -3 L L]
[ L -3 -3 L] [-3 -3 -3 -3] [-3 -3 L -3]
[ L L -3 L] [-3 L -3 -3] [-3 L -3 -3] [-3 L L -3]
Q Table
L = Lava
[ ← ↓ → ↑ ]

40. #MinecraftRL @choas
[99 0 0 0] [78 -1 -1 0] [-1 -1 L -1]
[ L -1 -1 78] [61 -1 -1 -1] [48 -1 L -1]
[ L -2 -2 61] [-2 -2 -2 48] [-1 L L -1]
[ L L -2 48] [-2 -3 L 37]
[ L -3 -3 29] [-3 -3 L L]
[ L -4 -3 L] [-3 -3 -3 -3] [-3 -3 L -3]
[ L L -4 L] [-3 L -3 -3] [-3 L -3 -3] [-3 L L -3]
Q Table
L = Lava
[ ← ↓ → ↑ ]

41. #MinecraftRL @choas
[99 0 0 0] [78 -1 -1 0] [-1 -1 L -1]
[ L -1 -1 78] [61 -1 -1 -1] [48 -1 L -1]
[ L -2 -2 61] [-2 -2 -2 48] [-1 L L -1]
[ L L -2 48] [-2 -3 L 37]
[ L -3 -3 29] [-3 -3 L L]
[ L -4 -3 L] [-3 -3 -3 22] [-3 -3 L -3]
[ L L -4 L] [-3 L -3 -3] [-3 L -3 -3] [-3 L L -3]
Q Table
L = Lava
[ ← ↓ → ↑ ]

42. #MinecraftRL @choas
[99 0 0 0] [78 -1 -1 0] [-1 -1 L -1]
[ L -1 -1 78] [61 -1 -1 -1] [48 -1 L -1]
[ L -2 -2 61] [-2 -2 -2 48] [-1 L L -1]
[ L L -2 48] [-2 -3 L 37]
[ L -3 -3 29] [-3 -3 L L]
[ L -4 16 L] [-3 -3 -3 22] [-3 -3 L -3]
[ L L -4 L] [-4 L -3 -3] [-3 L -3 -3] [-3 L L -3]
Q Table
L = Lava
[ ← ↓ → ↑ ]

43. #MinecraftRL @choas
[99 0 0 0] [78 -1 -1 0] [-1 -1 L -1]
[ L -1 -1 78] [61 -1 -1 -1] [48 -1 L -1]
[ L -2 -2 61] [-2 -2 -2 48] [-1 L L -1]
[ L L -2 48] [-2 -3 L 37]
[ L -3 -3 29] [-3 -3 L L]
[ L -4 16 L] [-3 -3 -3 22] [-3 -3 L -3]
[ L L -4 L] [-4 L -3 12] [-3 L -3 16] [-3 L L -3]
Q Table
L = Lava
[ ← ↓ → ↑ ]

44. #MinecraftRL @choas
[99 0 0 0] [78 -1 -1 0] [-1 -1 L -1]
[ L -1 -1 78] [61 -1 -1 -1] [48 -1 L -1]
[ L -2 -2 61] [-2 -2 -2 48] [-1 L L -1]
[ L L -2 48] [-2 -3 L 37]
[ L -3 -3 29] [-3 -3 L L]
[ L -4 16 L] [-3 -3 -3 22] [-3 -3 L -3]
[ L L 8 L] [-4 L -3 12] [-3 L -3 16] [-3 L L -3]
Q Table
L = Lava
[ ← ↓ → ↑ ]

45. #MinecraftRL @choas
[99 0 0 0] [78 -1 -1 0] [-1 -1 L -1]
[ L -1 -1 78] [61 -1 -1 -1] [48 -1 L -1]
[ L -2 -2 61] [-2 -2 -2 48] [-1 L L -1]
[ L L -2 48] [-2 -3 L 37]
[ L -3 -3 29] [-3 -3 L L]
[ L -4 16 L] [-3 -3 -3 22] [-3 -3 L -3]
[ L L 8 L] [-4 L -3 12] [-3 L -3 16] [-3 L L -3]
ALPHA = 1.0 GAMMA = 0.8

46. #MinecraftRL @choas
[99 48 0 L] [48 0 0 0] [-1 0 L 0]
[ L 0 -1 97] [96 -1 -1 -1] [-1 -1 L -1]
[ L -1 -1 -1] [-1 -1 -1 92] [-1 L L -1]
[ L L -2 -1] [-2 -2 L 83]
[ L -3 -3 74] [-2 -4 L L]
[ L -5 -2 L] [-4 -4 -4 55] [-4 -4 L -4]
[ L L -1 L] [-6 L 11 -5] [-5 L -5 31] [-5 L L -4]
ALPHA = 0.5 GAMMA = 1.0 (40 moves)

47. #MinecraftRL @choas
[99 48 0 L] [48 0 0 0] [-1 0 L 0]
[ L 0 -1 97] [96 -1 -1 -1] [-1 -1 L -1]
[ L -1 -1 47] [-2 -1 -1 95] [-1 L L -1]
[ L L -2 -1] [-2 45 L 94]
[ L -3 -3 93] [-2 -4 L L]
[ L -5 -2 L] [-4 -4 -4 92] [-4 -4 L -4]
[ L L 88 L] [-6 L 90 -5] [-5 L -5 91] [-5 L L -4]
ALPHA = 0.5 GAMMA = 1.0 (60 moves)

48. !
!
!
!

49. ?

50. Deep Reinforcement Learning

51. #MinecraftRL @choas
Deep Reinforcement Learning
Supervised
Learning
Unsupervised
Learning
Reinforcement
Learning

52. #MinecraftRL @choas
Playing Atari with Deep Reinforcement Learning (arXiv:1312.5602)
https://youtu.be/TmPfTpjtdgg

53. #MinecraftRL @choas

54. #MinecraftRL @choas

55. #MinecraftRL @choas
12 Classes

56. #MinecraftRL @choas
### based on arXiv:1312.5602 (page 6)
model = Sequential()
model.add(Conv2D(16, (8, 8), strides=(4, 4), input_shape=input_shape))
model.add(Activation('relu'))
model.add(Conv2D(32, (4, 4), strides=(2, 2)))
model.add(Activation(‘relu'))
model.add(Flatten())
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dense(12, activation=‘sigmoid')) # 12 classes / actions
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
Keras Model

57. #MinecraftRL @choas
Deep Reinforcement Learning Demo

58. #MinecraftRL @choas
Take-away

59. Links
The Malmo Platform for Artificial Intelligence Experimentation. Proc. 25th International Joint
Conference on Artificial Intelligence http://www.ijcai.org/Proceedings/2016
Project Malmo https://www.microsoft.com/en-us/research/project/project-malmo/
Project Malmo (Github) https://github.com/Microsoft/malmo
Reinforcement Learning: An Introduction - ISBN-13: 978-0262193986
2nd Version online
YouTube RL Course by David Silver

60. #MinecraftRL @choas
Thank you. Hi Lars …

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