https://telecombcn-dl.github.io/2017-dlai/ Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.

1. [course site]
Xavier Giro-i-Nieto
xavier.giro@upc.edu
Associate Professor
Universitat Politecnica de Catalunya
Technical University of Catalonia
Reinforcement Learning
Day 7 Lecture 2
#DLUPC

2. 2
Acknowledegments
Bellver M, Giró-i-Nieto X, Marqués F, Torres J. Hierarchical Object Detection with Deep Reinforcement Learning. In Deep
Reinforcement Learning Workshop, NIPS 2016. 2016.

3. 3
Acknowledegments
Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.

4. 4
Outline
1. Motivation
2. Architecture
3. Markov Decision Process (MDP)
4. Deep Q-learning
5. RL Frameworks
6. Learn more

5. 5
Outline
1. Motivation
2. Architecture
3. Markov Decision Process (MDP)
4. Deep Q-learning
5. RL Frameworks
6. Learn more

6. 6
Motivation
What is Reinforcement Learning ?
“a way of programming agents by reward and punishment without needing to
specify how the task is to be achieved”
[Kaelbling, Littman, & Moore, 96]
Kaelbling, Leslie Pack, Michael L. Littman, and Andrew W. Moore. "Reinforcement learning: A survey." Journal of artificial
intelligence research 4 (1996): 237-285.

7. Yann Lecun’s Black Forest cake
7
Motivation

8. We can categorize three types of learning procedures:
1. Supervised Learning:
= ƒ( )
2. Unsupervised Learning:
ƒ( )
3. Reinforcement Learning (RL):
= ƒ( )
8
Predict label y corresponding to
observation x
Estimate the distribution of
observation x
Predict action y based on
observation x, to maximize a future
reward z
Motivation

9. We can categorize three types of learning procedures:
1. Supervised Learning:
= ƒ( )
2. Unsupervised Learning:
ƒ( )
3. Reinforcement Learning (RL):
= ƒ( )
9
Motivation

10. 10
Outline
1. Motivation
2. Architecture
3. Markov Decision Process (MDP)
4. Deep Q-learning
5. RL Frameworks
6. Learn more

11. 11
Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller.
"Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602 (2013).

12. 12Bernhard Schölkkopf, “Learning to see and act” Nature 2015.
Motivation

13. 13
Outline
1. Motivation
2. Architecture
3. Markov Decision Process (MDP)
○ Policy
○ Optimal Policy
○ Value Function
○ Q-value function
○ Optimal Q-value function
○ Bellman equation
○ Value iteration algorithm
4. Deep Q-learning
5. RL Frameworks
6. Learn more

14. 14Figure: UCL Course on RL by David Silver
Architecture

15. 15Figure: UCL Course on RL by David Silver
Environment
Architecture

16. 16Figure: UCL Course on RL by David Silver
Environment
state (st
)
Architecture

17. 17Figure: UCL Course on RL by David Silver
Environment
state (st
)
Architecture

18. 18Figure: UCL Course on RL by David Silver
Environment
Agent
state (st
)
Architecture

19. 19Figure: UCL Course on RL by David Silver
Environment
Agent
action (At
)state (st
)
Architecture

20. 20Figure: UCL Course on RL by David Silver
Environment
Agent
action (At
)state (st
)
Architecture

21. 21Figure: UCL Course on RL by David Silver
Environment
Agent
action (At
)
reward (rt
)
state (st
)
Architecture

22. 22Figure: UCL Course on RL by David Silver
Environment
Agent
action (At
)
reward (rt
)
state (st
)
Architecture
Reward is given to
the agent delayed
with respect to
previous states and
actions !

23. 23Figure: UCL Course on RL by David Silver
Environment
Agent
action (At
)
reward (rt
)
state (st+1
)
Architecture

24. 24Figure: UCL Course on RL by David Silver
Environment
Agent
action (At
)
reward (rt
)
state (st+1
)
Architecture GOAL: Complete the
game with the
highest score.

25. 25Figure: UCL Course on RL by David Silver
Environment
Agent
action (At
)
reward (rt
)
state (st+1
)
Architecture GOAL: Learn how to
take actions to
maximize
accumulative reward

26. 26
Other problems that can be formulated with a RL architecture.
Cart-Pole Problem
Objective: Balance a pole on top of a movable car
Architecture
Slide credit: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.

27. 27
Architecture
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
Environment
Agent
action (At
)
reward (rt
)
state (st
) Angle
Angular speed
Position
Horizontal velocity
Horizontal force
applied in the car1 at each time
step if the pole is
upright

28. 28
Other problems that can be formulated with a RL architecture.
Robot Locomotion
Objective: Make the robot move forward
Architecture
Schulman, John, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. "High-dimensional continuous control using generalized
advantage estimation." ICLR 2016 [project page]

29. 29
Architecture
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
Environment
Agent
action (At
)
reward (rt
)
state (st
) Angle and position
of the joints
Torques applied
on joints1 at each time
step upright +
forward
movement

30. 30
Schulman, John, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. "High-dimensional continuous control using generalized
advantage estimation." ICLR 2016 [project page]

31. 31
Outline
1. Motivation
2. Architecture
3. Markov Decision Process (MDP)
○ Policy
○ Optimal Policy
○ Value Function
○ Q-value function
○ Optimal Q-value function
○ Bellman equation
○ Value iteration algorithm
4. Deep Q-learning
5. RL Frameworks
6. Learn more

32. 32
Markov Decision Processes (MDP)
Markov Decision Processes provide a formalism for reinforcement learning
problems.
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
Markov property:
Current state completely
characterises the state of the world.

33. 33
Markov Decision Processes (MDP)
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
S A R P ४

34. 34
Markov Decision Processes (MDP)
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
S A R P ४
Environment
samples initial
state s0
~ p(s0
)
Agent
selects
action at
Environment samples
next state st+1
~ P ( .| st
, at
)
Environment samples
reward rt
~ R(. | st
,at
) reward
(rt
)
state (st
) action (at
)

35. 35
MDP: Policy
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
S A R P ४
Agent
selects
action at
policy π
A Policy π is a function S ➝ A that specifies which action to take in each state.

36. 36
MDP: Policy
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
Agent
selects
action at
policy π
A Policy π is a function S ➝ A that specifies which action to take in each state.
GOAL: Learn how
to take actions to
maximize reward
Agent
GOAL: Find policy π* that
maximizes the cumulative
discounted reward:
MDP

37. 37
Other problems that can be formulated with a RL architecture.
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
MDP: Policy
Grid World (a simple MDP)
Objective: reach one of the terminal
states (greyed out) in least number of
actions.

38. 38Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
Environment
Agent
action (At
)
reward (rt
)
state (st
)
Each cell is a state:
A negative
“reward” (penalty)
for each transition
rt
= r = -1
MDP: Policy

39. 39Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
MDP: Policy
Example: Actions resulting from applying a random policy on this Grid World
problem.

40. 40Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
Exercise: Draw the actions resulting from applying an optimal policy in this Grid
World problem.
MDP: Optimal Policy π*

41. 41Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
Solution: Draw the actions resulting from applying an optimal policy in this Grid World
problem.
MDP: Optimal Policy π*

42. 42
MDP: Optimal Policy π*
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
How do we handle the randomness (initial
state s0
, transition probabilities, action...) ?
GOAL: Find policy π* that
maximizes the cumulative
discounted reward:
Environment
samples initial
state s0
~ p(s0
)
Agent selects
action at
~π
(.|st
)
Environment samples
next state st+1
~ P ( .| st
, at
)
Environment samples
reward rt
~ R(. | st
,at
) reward
(rt
)
state (st
) action (at
)

43. 43Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
How do we handle the randomness (initial
state s0
, transition probabilities, action) ?
GOAL: Find policy π* that
maximizes the cumulative
discounted reward:
The optimal policy π* will maximize the
expected sum of rewards:
initial
state
selected
action at t
sampled state for
t+1expected cumulative
discounted reward
MDP: Optimal Policy π*

44. 44
MDP: Policy: Value function Vπ
(s)
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
How to estimate how good state s is for a given policy π ?
With the value function at state s, Vπ
(s), the expected cumulative reward from
following policy π from state s.
“...from following policy π
from state s.”
“Expected
cumulative reward…””

45. 45
MDP: Policy: Q-value function Qπ
(s,a)
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
How to estimate how good a state-action pair (s,a) is for a given policy π ?
With the Q-value function at state s and action a, Qπ
(s,a), the expected cumulative
reward from taking action a in state s, and then following policy π.
“...from taking action a in state s
and then following policy π.”
“Expected
cumulative reward…””

46. 46
MDP: Policy: Optimal Q-value function Q*
(s,a)
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
The optimal Q-value function at state s and action, Q*
(s,a), is the maximum
expected cumulative reward achievable from a given (state, action) pair:
choose the policy that
maximizes the expected
cumulative reward
(From the previous page)
Q-value function

47. 47
MDP: Policy: Bellman equation
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
Q*
(s,a) satisfies the following Bellman equation:
Maximum expected
cumulative reward for
future pair (s’,a’)
FUTURE REWARD
(From the previous page)
Optimal Q-value function
reward for
considered
pair (s,a)
Maximum expected
cumulative reward
for considered pair
(s,a)
Expectation
across possible
future states s’
(randomness) discount
factor

48. 48
MDP: Policy: Bellman equation
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
Q*
(s,a) satisfies the following Bellman equation:
The optimal policy π* corresponds to taking the best action in any state according to
Q*.
GOAL: Find policy π* that
maximizes the cumulative
discounted reward:
select action a’ that maximizes
expected cumulative reward

49. 49
MDP: Policy: Solving the Optimal Policy
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
Value iteration algorithm: Estimate the Bellman equation with an iterative update.
The iterative estimation Qi
(s,a) will converge to the optimal Q*(s,a) as i ➝ ∞.
(From the previous page)
Bellman Equation
Updated Q-value
function
Current Q-value for
future pair (s’,a’)

50. 50
MDP: Policy: Solving the Optimal Policy
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
Qi
(s,a) will converge to the optimal Q*(s,a) as i ➝ ∞.
Updated Q-value for
current pair (s,a)
Current Q-value for
next pair (s’,a’)
This iterative approach is not scalable because it
requires computing Qi
(s,a) for every state-action pair.
Eg. If state is current game pixels, computationally unfeasible to compute
Qi
(s,a) for the entire state space !

51. 51
MDP: Policy: Solving the Optimal Policy
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
This iterative approach is not scalable because it
requires computing Q(s,a) for every state-action pair.
Eg. If state is current game pixels, computationally unfeasible to compute
Q(s,a) for the entire state space !
Solution: Use a deep neural network as
an function approximator of Q*(s,a).
Q(s,a,Ө) ≈ Q*(s,a)
Neural Network parameters

52. 52
Outline
1. Motivation
2. Architecture
3. Markov Decision Process (MDP)
4. Deep Q-learning
○ Forward and Backward passes
○ DQN
○ Experience Replay
○ Examples
5. RL Frameworks
6. Learn more
○ Coming next…

53. 53
Deep Q-learning
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
The function to approximate is a Q-function that satisfies the Bellman equation:
Q(s,a,Ө) ≈ Q*(s,a)

54. 54
Deep Q-learning
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
The function to approximate is a Q-function that satisfies the Bellman equation:
Q(s,a,Ө) ≈ Q*(s,a)
Forward Pass
Loss function:
Sample a (s,a) pair Predicted Q-value
with Өi
Sample a
future state s’
Predict Q-value with Өi-1

55. 55
Deep Q-learning
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
Train the DNN
to approximate
a Q-value
function that
satisfies the
Bellman
equation

56. 56
Deep Q-learning
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
Must compute
reward during
training

57. 57
Deep Q-learning
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
Backward Pass
Gradient update (with respect to Q-function parameters Ө):
Forward Pass
Loss function:

58. 58Source: Tambet Matiisen, Demystifying Deep Reinforcement Learning (Nervana)
Deep Q-learning: Deep Q-Network DQN
Q(s,a,Ө) ≈ Q*(s,a)

59. 59Source: Tambet Matiisen, Demystifying Deep Reinforcement Learning (Nervana)
Deep Q-learning: Deep Q-Network DQN
Q(s,a,Ө) ≈ Q*(s,a)
efficiency Single
Feed
Forward
Pass
A single feedforward pass to compute the Q-values
for all actions from the current state (efficient)

60. 60
Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves et al.
"Human-level control through deep reinforcement learning." Nature 518, no. 7540 (2015): 529-533.
Deep Q-learning: Deep Q-Network DQN
Number of
actions between
4-18, depending
on the Atari
game

61. 61
Deep Q-learning: Deep Q-Network DQN
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
Q(st
, ⬅), Q(st
, ➡), Q(st
, ⬆), Q(st
,⬇ )

62. 62
Deep Q-learning: Experience Replay
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
Learning from batches of consecutive samples
is problematic:
● Samples are too correlated ➡
inefficient learning
● Q-network parameters determine the next
training samples ➡
can lead to bad feedback loops.

63. 63
Deep Q-learning: Experience Replay
Slide concept: Serena Yeung, “Deep Reinforcement Learning”. Stanford University CS231n, 2017.
Experience replay:
● Continually update a replay memory table
of transitions (st
, at
, rt
, st+1
) as game
(experience) episodes are played.
● Train a Q-network on random minibatches
of transitions from the replay memory,
instead of consecutiev samples.

64. 64Andrej Karpathy, “ConvNetJS Deep Q Learning Demo”
Deep Q-learning: Demo

65. 65
Miriam Bellver, Xavier Giro-i-Nieto, Ferran Marques, and Jordi Torres. "Hierarchical Object Detection
with Deep Reinforcement Learning." Deep Reinforcement Learning Workshop NIPS 2016.
Deep Q-learning: DQN: Computer Vision
Method for performing hierarchical object detection in images guided by a
deep reinforcement learning agent.
OBJECT
FOUND

66. 66
Deep Q-learning: DQN: Computer Vision
State: The agent will decide which action to choose based on:
● visual description of the current observed region
● history vector that maps past actions performed

67. 67
Deep Q-learning: DQN: Computer Vision
Reward:
Reward for movement actions
Reward for terminal action

68. 68
Deep Q-learning: DQN: Computer Vision
Actions: Two kind of actions:
● movement actions: to which of the 5 possible regions defined by the
hierarchy to move
● terminal action: the agent indicates that the object has been found

69. 69
Miriam Bellver, Xavier Giro-i-Nieto, Ferran Marques, and Jordi Torres. "Hierarchical Object Detection
with Deep Reinforcement Learning." Deep Reinforcement Learning Workshop NIPS 2016.
Deep Q-learning: DQN: Computer Vision

70. 70
Outline
1. Motivation
2. Architecture
3. Markov Decision Process (MDP)
4. Deep Q-learning
5. RL Frameworks
6. Learn more

71. 71
RL Frameworks
OpenAI Gym + keras-rl
+
keras-rl
keras-rl implements some state-of-the
art deep reinforcement learning
algorithms in Python and seamlessly
integrates with the deep learning
library Keras. Just like Keras, it works
with either Theano or TensorFlow,
which means that you can train your
algorithm efficiently either on CPU or
GPU. Furthermore, keras-rl works with
OpenAI Gym out of the box.
Slide credit: Míriam Bellver

72. 72
OpenAI
Universe
environment
RL Frameworks

73. 73
Outline
1. Motivation
2. Architecture
3. Markov Decision Process (MDP)
4. Deep Q-learning
5. RL Frameworks
6. Learn more

74. 74
Deep Learning TV,
“Reinforcement learning - Ep. 30”
Siraj Raval, Deep Q Learning for Video
Games
Learn more

75. Emma Brunskill, Stanford CS234: Reinforcement Learning
Learn more

76. David Silver, UCL COMP050, Reinforcement Learning
Learn more

77. Nando de Freitas, “Machine Learning” (University of Oxford)
Learn more

78. 78
Pieter Abbeel and John Schulman, CS 294-112 Deep Reinforcement Learning,
Berkeley.
Slides: “Reinforcement Learning - Policy Optimization” OpenAI / UC Berkeley (2017)
Learn more

79. 79
Learn more
Slide credit: Míriam Bellver
actor
state
critic
‘q-value’action (5)
state
action (5)
actor performs an
action
critic assesses how good the action was, and the
gradients are used to train the actor and the critic
Actor-Critic algorithm
Grondman, Ivo, Lucian Busoniu, Gabriel AD Lopes, and Robert Babuska. "A survey of actor-critic reinforcement learning: Standard and natural
policy gradients." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42, no. 6 (2012): 1291-1307.

80. ● Evolution Strategies
Learn more

81. 81
Outline
1. Motivation
2. Architecture
3. Markov Decision Process (MDP)
○ Policy
○ Optimal Policy
○ Value Function
○ Q-value function
○ Optimal Q-value function
○ Bellman equation
○ Value iteration algorithm
4. Deep Q-learning
○ Forward and Backward passes
○ DQN
○ Experience Replay
○ Examples
5. RL Frameworks
6. Learn more
○ Coming next…

82. Conclusions
Reinforcement Learning
● There is no supervisor, only reward
signal
● Feedback is delayed, not
instantaneous
● Time really matters (sequential, non
i.i.d data)
Slide credit: UCL Course on RL by David Silver

83. 83
Coming next...
https://www.theguardian.com/technology/2014/jan/27/google-acquires-uk-artificial-intelligence-startup-deepmind

84. 84
Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I.,
Panneershelvam, V., Lanctot, M. and Dieleman, S., 2016. Mastering the game of Go with deep neural networks and tree
search. Nature, 529(7587), pp.484-489
Coming next...

85. 85
Greg Kohs, “AlphaGo” (2017)
Coming next...

86. 86
Vinyals, O., Ewalds, T., Bartunov, S., Georgiev, P., Vezhnevets, A.S., Yeo, M., Makhzani, A., Küttler, H.,
Agapiou, J., Schrittwieser, J. and Quan, J., 2017. Starcraft ii: A new challenge for reinforcement learning.
arXiv preprint arXiv:1708.04782. [Press release]
Coming next...

87. 87
Vinyals, O., Ewalds, T., Bartunov, S., Georgiev, P., Vezhnevets, A.S., Yeo, M., Makhzani, A., Küttler, H.,
Agapiou, J., Schrittwieser, J. and Quan, J., 2017. Starcraft ii: A new challenge for reinforcement learning.
arXiv preprint arXiv:1708.04782. [Press release]
Coming next...

88. 88
Coming next...
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