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Solving Blackjack with Q-Learning #
Summary
The article covers how to use Q-learning to solve the Blackjack-v1 environment. It explains how the environment works and how to take actions within it. It also covers how to build an agent and how to train it. Finally, it shows how to visualize the training.
Q&As
What is the objective of the game?
The objective of the game is to win by having a card sum that is greater than the dealer's without exceeding 21.
What is the player's current sum?
The player's current sum is the player's current sum.
What is the value of the dealer's face-up card?
The value of the dealer's face-up card is the value of the dealer's face-up card.
What is the reward the player will receive after taking an action?
The reward the player will receive after taking an action is the reward the player will receive after taking an action.
What is the boolean variable that indicates whether the environment has terminated?
The boolean variable that indicates whether the environment has terminated is the boolean variable that indicates whether the environment has terminated.
AI Comments
👍 Great tutorial on how to use Q-learning to solve the Blackjack-v1 environment! I learned a lot from reading this and am excited to try it out myself.
👎 This tutorial is way too long and detailed. I just want to get started solving the environment, not read a novel on the subject.
AI Discussion
Me: It's about solving the Blackjack-v1 environment using Q-learning.
Friend: That's interesting. What are the implications of the article?
Me: The implications of the article are that you can use Q-learning to solve the Blackjack-v1 environment.
Action items
- Train an agent to solve the Blackjack-v1 environment using the Q-learning algorithm.
- Try increasing the number of episodes and lowering the learning rate to see if the agent can converge to the optimal policy.
- Use the Monte Carlo ES algorithm to solve the Blackjack-v1 environment and compare the results to those of the Q-learning algorithm.
Technical terms
- Blackjack
- a popular casino card game
- Q-learning
- a model-free RL algorithm
- observation
- the state of the environment
- action
- a move that the agent can make
- next_state
- the state of the environment after taking an action
- reward
- a value assigned to an action that indicates how good or bad it is
- terminated
- a boolean variable that indicates whether or not the environment has terminated
- truncated
- a boolean variable that indicates whether the episode ended by early truncation
- info
- a dictionary that might contain additional information about the environment