強化學習在 Atari 遊戲, 圍棋, 西洋棋, 日本將棋上攻城掠地都取得了超越人類水平的成績, 其中許多技術都具有里程碑的重要性, 並且能夠應用到其他領域, 本次演講將整理回顧從解決 Atari 一個簡單遊戲到解決 Atari 所有 57 個遊戲的關鍵, 以及 AlphaGo, AlphaGo Zero, Alpha Zero, MuZero 的發展歷程。

1. 強化學習的王者之旅
余方國 博士
04/11/2023

2. 2
強化學習的王者之旅
https://www.imdb.com/title/tt0108065/?ref_=ttmi_tt

3. 3
強化學習的王者之旅
MuZero
Alpha Zero
Gym
Gym

4. 4
Atari Games
Pong Breakout Phoenix
https://www.gymlibrary.dev/
https://gymnasium.farama.org/

5. 5
Reinforcement Learning Framework
ENVIRONMENT
AGENT
State Action Reward
(s1 → a1 → r1)→ (s2 → a2 → r2)→ (s3 → a3 → r3)→ …
Making Sequential Decisions to Maximize Long-Term Rewards

6. 6
Atari Breakout in OpenAI Gym
import gym
env = gym.make("ALE/Breakout-v5", render_mode="human")
state, info = env.reset()
for index in range(1000):
action = env.action_space.sample() # action by random or policy
state, reward, terminated, truncated, info = env.step(action)
if terminated or truncated:
state, info = env.reset()
env.close()
https://www.gymlibrary.dev/
https://gymnasium.farama.org/

7. 7
State/Action/Reward in Atari Breakout
State:
●
(210, 160, 3) - image
Action:
●
0 - NO OP
●
1 - FIRE
●
2 - RIGHT
●
3 - LEFT
Reward:
●
Red - 7 points
●
Orange - 7 points
●
Yellow - 4 points
●
Green - 4 points
●
Aqua - 1 point
●
Blue - 1 point
https://www.gymlibrary.dev/
https://gymnasium.farama.org/

8. 8
From One Game to All The Games in Atari
https://www.deepmind.com/blog/agent57-outperforming-the-human-atari-benchmark

9. 9
A Journey to Artificial General Intelligence
https://www.assemblyai.com/blog/reinforcement-learning-with-deep-q-learning-explained/
https://www.deepmind.com/blog/agent57-outperforming-the-human-atari-benchmark
DQN/2015
R2D2/2019
NGU/2019
Agent57/2020

10. 10
OpenAI Gym Taxi-v3 : State/Action/Reward
State:
●
Number of Variable : 1
●
Range of Variable : [1, 500]
●
25 taxi positions x 5 passenger positions x 4 destination locations
Action:
●
0 : move south
●
1 : move north
●
2 : move east
●
3 : move west
●
4 : pickup passenger
●
5 : drop off passenger
Reward:
●
+20 : delivering passenger
●
-10 : pickup/dropoff illegally
●
-1 : per step unless other rewards is triggered
https://www.gymlibrary.dev/environments/toy_text/taxi/

11. 11
OpenAI Gym Taxi-v3 : Q Table
(500 x 6)
https://www.gocoder.one/blog/rl-tutorial-with-openai-gym

12. 12
Q Learning (with epsilon greedy policy)
3. exploitation
1. initialize Q table
4. exploration
5. action
2. state
8. update Q table
6. next state
7. reward
https://www.cs.toronto.edu/~rgrosse/courses/csc311_f21/

13. 13
Limitation of Q Table
representation
scalability

14. 14
Deep Q Network (DQN) Architecture (1/2)
Ref : Human-level control through deep reinforcement learning

15. 15
Deep Q Network (DQN) Architecture (2/2)
Ref : Massively Parallel Methods for Deep Reinforcement Learning

16. 16
Deep Q Learning (with experience replay and dual networks)
1. initialize replay memory
5. store transition in replay memory
6. get batch from replay memory
2. initialize main network
3. initialize target network
4. epsilon greedy policy from main network
7. calculate error between two networks
8. synchronize two networks
Ref : Human-level control through deep reinforcement learning

17. 17
Deep Q Network (DQN) on Breakout
Artificial Intelligence and the Future - Demis Hassabis/DeepMind
https://youtu.be/zYII3AOSgo8?t=2236

18. 18
Deep Q Network (DQN) Benchmark
Ref : Human-level control through deep reinforcement learning

19. 19
Four Tough Games in Atari
Pitfall Solaris Skiing Montezuma’s Revenge
Problems : long-term credit assignment and exploitation/exploration tradeoff
Solutions : intrinsic motivation, meta-controller, short-term/episodic memory, distributed agents, etc.
https://www.deepmind.com/blog/agent57-outperforming-the-human-atari-benchmark

20. 20
Distributed Reinforcement Learning
Agent57
Gorila
https://arxiv.org/abs/2003.13350
https://arxiv.org/abs/1507.04296

21. 21
How Well Can Agent57 Do?
https://www.deepmind.com/blog/agent57-outperforming-the-human-atari-benchmark

22. 22
Deep Q Network and Brain Activity

23. 23
Policy Gradient on Atari Pong
https://www.youtube.com/watch?v=tqrcjHuNdmQ

24. 24
Reinforcement Learning at DeepMind
https://analyticsindiamag.com/all-hail-the-king-of-reinforcement-learning-deepmind/

25. 25
Mastering Go at DeepMind
https://analyticsindiamag.com/all-hail-the-king-of-reinforcement-learning-deepmind/

26. 26
A Journey to Artificial General Intelligence
https://www.deepmind.com/blog/muzero-mastering-go-chess-shogi-and-atari-without-rules
https://www.youtube.com/watch?v=lVMgxtm5L-U

27. 27
AlphaGo, AlphaGo Zero, Alpha Zero, MuZero
AlphaGo Zero, Nature, 2017
AlphaZero, Science, 2018 MuZero, Nature, 2020
AlphaGo, Nature, 2016

28. 28
AlphaGo Fan/Lee/Master
●
European Go Champion Fan Hui — 5:0
●
South Korean professional Go player Lee Sedol — 4:1
●
Online games with players from China/Korea/Japan — 60:0
●
Chinese professional Go player Ke Jie — 3:0
https://www.youtube.com/watch?v=lVMgxtm5L-U
https://www.youtube.com/watch?v=LX8Knl0g0LE

29. 29
AlphaGo Fan/Lee/Master
●
European Go Champion Fan Hui — 5:0
●
South Korean professional Go player Lee Sedol — 4:1
●
Online games with players from China/Korea/Japan — 60:0
●
Chinese professional Go player Ke Jie — 3:0
https://www.youtube.com/watch?v=lVMgxtm5L-U
https://www.youtube.com/watch?v=WXuK6gekU1Y

30. 30
AlphaGo Inputs and Policy/Value Networks
https://www.slideshare.net/ckmarkohchang/alphago-in-depth

31. 31
AlphaGo Monte Carlo Tree Search
https://www.slideshare.net/ckmarkohchang/alphago-in-depth

32. 32
AlphaZero Training Process
Self-Play
Train
Value
Network
Train
Policy
Network
https://www.youtube.com/watch?v=lVMgxtm5L-U

33. 33
AlphaZero Network for Chess
Ref: Acquisition of Chess Knowledge in AlphaZero
AlphaGo
• Two networks: policy network and value network
• Conv/ReLu-based layer structure
AlphaZero
• One network with two heads: policy and value
• ResNet-based layer structure

34. 34
AlphaGo Zero Performance Benchmark
https://thirdeyedata.ai/how-to-build-your-own-alphazero-ai-using-python-and-keras/

35. 35
MuZero Training Process
h: representation
f: prediction
g: dynamics
Ref: Mastering Atari, Go, chess and shogi by planning with a learned model

36. 36
MuZero Performance Benchmark
Ref: Mastering Atari, Go, chess and shogi by planning with a learned model

37. 37
MuZero for Self-Driving Car at Tesla
https://www.youtube.com/watch?v=j0z4FweCy4M

38. 38
AlphaGo to AlphaStar by David Silver
Deep Reinforcement Learning from AlphaGo to AlphaStar - London Machine Learning Meetup

39. 39
強化學習的王者之旅
MuZero
Alpha Zero
Gym
Gym
深度強化學習
通用人工智慧

40. 40
強化學習的王者之旅
演講資料
fangkuoyu@gmail.com
博客帳號