This document provides an introduction and overview of reinforcement learning. It begins with a syllabus that outlines key topics such as Markov decision processes, dynamic programming, Monte Carlo methods, temporal difference learning, deep reinforcement learning, and active research areas. It then defines the key elements of reinforcement learning including policies, reward signals, value functions, and models of the environment. The document discusses the history and applications of reinforcement learning, highlighting seminal works in backgammon, helicopter control, Atari games, Go, and dialogue generation. It concludes by noting challenges in the field and prominent researchers contributing to its advancement.

1. An Introduction to
Reinforcement Learning
Jie-Han Chen
NetDB, National Cheng Kung University
3/27, 2018 @ National Cheng Kung University, Taiwan
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2. The content in this lecture were borrowed from:
1. Rich Sutton’s textbook
2. David Silver’s Reinforcement Learning class in UCL
3. Sergey Levine’s Deep Reinforcement Learning class in UCB
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Disclamier

3. Syllabus
● Introduction to Reinforcement Learning
● Markov Decision Process
● Dynamic Programming
● Monte Carlo method
● Temporal Difference method
● Deep Reinforcement Learning
● Policy Gradient
● Hierarchical Reinforcement Learning and Multiagent Reinforcement Learning
● Active Research Issue
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4. Resources
Textbooks:
● Reinforcement Learning: An Introduction, Sutton and Barto
● Algorithms for Reinforcement Learning, Szepesvari
Course:
● CS 294 Deep Reinforcement Learning, Berkeley
● David Silver’s Reinforcement Learning course, UCL
● CMU 10703 Deep Reinforcement Learning and Control, CMU
● Shan-Hung Wu’s Deep Learning course in NTHU
All of them are our reference materials in this lecture.
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5. Outline
● Syllabus
● Introduction
● Elements of reinforcement learning and its objective
● History of RL
● Applications
● The challenge and active research fields in RL
● Research institute and notable researchers
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6. Machine Learning
From David Silver’s RL course 6

7. Introduction to Reinforcement Learning
Reinforcement learning is a learning framework different from supervised learning
and unsupervised learning.
It is composed of series of perception and interaction between agent and
environment.
From Sutton’s book 7

8. Agent and Environment
At each step t the agent:
● Receives scalar reward Rt
● Receives observaiotn Ot
● Executes action At
The environment:
● Receives action At
● Emits observation Ot+1
● Emits scalar reward Rt+1
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9. Introduction to Reinforcement Learning
Reinforcement Learning is often used to solve sequential decision problem.
● Goal: select actions to maximize total future reward
● Action may have long term consequences
● Reward may be delayed
● It may be better to sacrifice immediate reward to gain more long-term reward
● Eg:
○ A financial investiment
○ Chess game
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10. Supervised Learning & Unsupervised Learning
The input data are independent (i.i.d).
Current output will not affect the next
input.
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11. Reinforcement Learning
The agent’s action do affect the data
received in the future.
Figure from Wikipedia, made by waldoalvarez11

12. Introduction to Reinforcement Learning
● In reinforcement learning the
agent learns from trial and error.
● The better experience make the
agent learn better policy.
● What kind of experience is
better?
The image is from :
http://www.homemeeting.us/franktmc/maze_2.jpg
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13. Elements of reinforcement learning
● Policy
● Reward signal
● Value function
● Model of environment (optional)
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14. Elements of reinforcement learning - policy
Policy
● Define the learning agents’ way of behaving at a given time. Could be a
simple function or lookup table or search process
● Often denoted by
● Could be deterministic or stochastic
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15. Elements of reinforcement learning - policy
If you are Russell Westbrook, and now
is defended by James Harden. With
this situation, you have 3 choices:
● Cut
● Shoot
● Pass
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16. Stochastic policy
Probability
Action
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17. Deterministic policy
Probability
Action
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18. Policies - Action space
In reinforcement learning, we can categorize the problem by the action space into
2 types.
● Discrete action space
● Continuous action space
In previous example, the decision or the action are in discrete space, but there are
many example of continuous control, eg: robotic arm. The stochastic policy of
continuous control problem would like a probability density function.
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19. Elements of reinforcement learning - reward
Reward: r / Rt
● Defines the goal in a reinforcement learning problem
● Indicates how well agent is doing at step t
● Immediately percepted from the environment
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20. Elements of reinforcement learning - reward
+2
0 or -0.2?
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21. Elements of reinforcement learning - reward
In chess or Go, the reward is defined
by its outcome.
● Win: +1
● Draw: 0
● Lose: -1
In most steps, we don’t receive any
reward(value = 0). It’s a kind of sparse
reward problem.
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22. Elements of reinforcement learning - reward
If we want to reach the goal by less
steps, we often define the reward to
-1 when you take a step.
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23. Elements of reinforcement learning - value function
Value function
● Indicates which decision is good in the long run.
● There are two forms:
○ state-value function
○ action-value function
● Unlike reward, value function is an estmated value.
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24. Elements of reinforcement learning - value function
The game comes to 99 vs 98(our) and just
left 5 seconds to the end of the game.
Now, If you need to throw in in midfield,
which one would you pass the ball to?
1. 櫻木花道
2. 三井壽
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25. Elements of reinforcement learning - model
Model of environments (optional)
● Use something to mimic the behavior of the environment.
● Allow inferences to be made about how the environment will behave.
(planning)
● Methods for solving reinforcement learning problems that use models for
planning are called model-based methods. The opposites are model-free
methods.
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26. Elements of reinforcement learning - model
Interaction, inferences
Learn the model
The image is from David Silver’s RL course 26

27. Just like ...
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28. Elements of reinforcement learning - model
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29. Elements of reinforcement learning - model
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30. Elements of reinforcement learning
● Policy
● Reward signal
● Value function
● Model of environment (optional)
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31. The objective of reinforcement learning
Reinforcement learning is a framework
of goal directed learning.
The objective of reinforcement learning
is to maximize accumulative rewards in
each task.
The image is from:
https://www.wikijob.co.uk/content/interview-advice/competencies/decision-making31

32. History of Reinforcement Learning
Reinforcement Learning is inspired by two domain knowledge
● Optimal control
● Biological learning system: Animal learning
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33. Optimal control
It is a mathematical optimization method for deriving control policies
especially under certain constraints.
The optimization method is largely due to the work of Lev Pontryagin and
Richard Bellman in the 1950s.
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34. Richard Bellman
Richard Bellman was an applied
mathematician, who introduced dynamic
programming in 1953.
Work:
● Bellman Equation
● Curse of dimensionality
● Bellman-Ford algorithm
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35. Animal Learning
● Teach dog - positive reward
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36. Animal Learning
● Teach dog - penalty (negative reward)
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37. Some question about RL
● Why do we need to learn Reinforcement Learning?
● What make Reinforcement Learning spring up like mushrooms?
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38. Backgammon (IBM, 1992)
Temporal difference learning and TD-Gammon, by
Gerald Tesauro, 1992
Gammon is 雙陸棋 in Chinese.
source: from wikipedia
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39. Autonomous Helicopter (Stanford, 2000)
The aerobatics fo helicopter has been studied from 2000 by Andrew Ng and
Pieter Abbeel in Stanford.
You can see more details on: http://heli.stanford.edu/39

40. Deep reinforcement learning in Atari game (2013)
Deep Q Network: proposed by V Mnih et al. It’s the first reinforcement learning
end-to-end model to combine deep learning with raw inputs.
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41. Deep reinforcement learning in Atari game (2013)
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42. Deep Reinforcement Learning for Robotic Manipulation
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43. AlphaGo (DeepMind, 2016)
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44. AlphaGo (DeepMind, 2016)
AlphaGo: David Silver, Aja Huang et al., use Monte Carlo Tree search (MCTS) and
deep reinforcement learning (policy gradient) to master the game of Go.
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45. AlphaGo Zero (DeepMind, 2017)
AlphaGo Zero: David Silver et al., use MCTS and policy iteration with ResNet with
2-head architecture to learn from scratch without human knowledge.
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46. 46
AlphaGo Zero (DeepMind, 2017)

47. Dota2 (OpenAI, 2017)
● Beats the world’s top professionals at 1v1 matches
● The bot learned from scratch by self-play
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48. Dota2 (OpenAI, 2017)
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49. Dota2 (OpenAI, 2017)
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50. Alibaba (Starcraft1, multiagent)
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51. Deep RL for Dialogue Generation (Li et al., 2016)
● RL agent generates more interactive responses
● RL agent tends to end a sentence with a question and hand the conversation
over to the user
● Next step: explore intrinsic rewards, large-scale training
From the slides on http://opendialogue.miulab.tw51

52. The Challenge of reinforcement learning
● Sparse reward issue
● Reward credit assignment
● Large space for exploration (trial-and-error)
● Imperfect information, partial observation
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53. Active research domain
● Multiagent reinforcement learning
● Hierarchical reinforcement learning
● Inverse reinforcement learning
● Multi-task Transfer learning in reinforcement learning
● Meta learning
● One-shot reinforcement learning
● Deep reinforcement learning in dialogue generation
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54. Research institute and notable researchers
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55. The research scientists in RL you must know!
● Richard S. Sutton
● David Silver
● Pieter Abbeel
● Sergey Levine
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56. Richard S. Sutton
● The founding father of reinforcement
learning
● Professor of Computer Science at University
of Alberta
● Temporal difference learning
● Dyna architecture
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57. David Silver
● The research scientist in DeepMind
● Lead researcher on AlphaGo and AlphaGo
Zero team
● Supervised by Sutton in Ph.D
● A professor in University College London
before
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58. Pieter Abbeel
● Professor in UC Berkeley
● Director of the UC Berkeley Robot Learning Lab
● Research scientist and advisor in OpenAI
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59. Sergey Levine
● Assistant Professor in UC Berkeley
● Research scientist in Google Brain
● Autonomous robots
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60. Question?
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