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Assignment Solution for Dr. David Silver's Reinforcement Learning Course

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Easy21

1 Implementation of Easy21: easy21-implement.py

2 Monte-Carlo Control in Easy21: easy21_mc_control.py

3 TD Learning in Easy21: easy21_sarsa_lambda.py

4 Linear Function Approximation in Easy21: easy21_sarsa_lambda_approx

5 Discussion

What are the pros and cons of bootstrapping in Easy21?
Pros: no need to wait until the end of an episode, accelerate the learning process, decrease the variance
Cons: may introduce bias

Would you expect bootstrapping to help more in blackjack or Easy21 ? Why?
Help more in Easy21, because it takes a longer time on average to finish an episode in easy21 due to the fact that a value of a card can be negative depending on its color.

What are the pros and cons of function approximation in Easy21?
Pros: memory saving, learning speed acceleration
Cons: can only solve the problem approximately since a function approximator cannot represent all the state-action values accurately

How would you modify the function approximator suggested in this section to get better results in Easy21?
Have no idea right now.

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