The document discusses reinforcement learning concepts like states, actions, rewards, and how reinforcement learning agents can learn optimal policies through trial-and-error interactions with an environment by maximizing rewards. It uses the example of learning to play the card game Exploding Kittens through reinforcement learning by receiving different rewards for drawing certain cards and learning which moves maximize long term rewards. The document also contrasts reinforcement learning with supervised learning and other machine learning techniques.

1. Reinforcement
Learning
The Exploding Kittens
Edition
Tarek Amr

2. Why Reinforcement Learning?
I learned after
playing many times;
That I‘m more likely to
win if I played this move
after that one.
No one kept telling me
make this or that move!

3. States, Actions and Rewards
St St+1
At At+1 St+2
Goal State
R

4. What’s a good reward
If getting an
Exploding Kitten card
gives me a reward of
-1;
What reward do I get
if I get a Defuse card?
And for a Nope card?

5. From Rewards, States get Values
And from
values comes
policies!

6. a State has a value (V)
St St+1
At At+1 St+2
Goal State
R
Vt Vt+1

7. or State/Action pair have a value (Q)
St St+1
At At+1 St+2
Goal State
R
Qt Qt+1

8. Temporal Difference; S-A-R-S-A
St St+1
At At+1 St+2
Goal State
R
Qt := Qt + α (Rt+1 + γ Qt+1 - Qt)

9. Epsilon Greedy
St
St+1At At+1 St+2
Goal State
RExploration vs Exploitation
Qt := Qt + α (Rt+1 + γ Qt+1 - Qt)

10. Deep Q Learning
State Feature1 State Feature2 Action Value
10 20 JUMP 0.5
20 15 DUCK 0.6
15 25 JUMP 0.8
Warning:Over simplification Ahead
This is a Q-Table;
What if there are too many States & Actions?

11. MDP, MC and TD
Markov Decision Process:
● You need to know the states and the transitions between them.
Monte Carlo (variance ↑):
● You wait till episode’s end, and re-assign values to states.
● No need to even know the states, we sample from the environment.
Temporal Difference (bias ↑):
● Update on the go. No need to even have goal states.

12. Let’s play the RL vs SL game
for (i=0; i<3; i++) {
● Pick a catawiki problem
● Should it be solved via
○ Reinforcement learning?
○ Supervised learning?
}