blackjack q learning algorithm in python

To implement a Q-learning algorithm for playing blackjack in Python, you can follow these steps:

  1. Set up the environment:

    • Define the state representation: In blackjack, the state can be defined by the player's current sum, the dealer's current showing card, and whether the player has a usable ace.
    • Define the action space: In blackjack, the player can either hit or stand.
    • Implement the reward function: Assign rewards based on the outcome of the game (e.g., +1 for winning, -1 for losing, 0 for a draw).

  2. Initialize the Q-table: Create a dictionary to store the Q-values for each state-action pair. Initialize all Q-values to 0.

  3. Define the Q-learning algorithm:

    • Choose an action: Use an epsilon-greedy strategy to select the action. With a probability (1-epsilon), choose the action with the highest Q-value for the current state. Otherwise, choose a random action.
    • Take the action: Implement the chosen action and observe the next state and reward.
    • Update the Q-value: Use the Q-learning update equation to update the Q-value for the current state-action pair.
    • Update the current state: Move to the next state.

  4. Repeat steps 3 for a specified number of episodes or until convergence.

Here is an example implementation of the Q-learning algorithm for blackjack in Python:

main.py
import numpy as np
import random

# Set up the environment
states = range(21)  # Player's current sum
actions = ["hit", "stand"]
q_table = {}

# Initialize the Q-table
for state in states:
    q_table[state] = {}
    for action in actions:
        q_table[state][action] = 0

def choose_action(state, epsilon):
    if random.uniform(0, 1) < epsilon:
        return random.choice(actions)
    else:
        return max(q_table[state], key=q_table[state].get)

def update_q_value(state, action, reward, next_state, alpha, gamma):
    q_table[state][action] += alpha * (reward + gamma * max(q_table[next_state].values()) - q_table[state][action])

def play_blackjack(num_episodes, epsilon, alpha, gamma):
    for episode in range(num_episodes):
        # Initialize the environment
        state = random.choice(states)
        done = False

        while not done:
            # Choose an action
            action = choose_action(state, epsilon)
            
            # Take the action
            if action == "hit":
                next_state = random.choice(states)
                if next_state > 21:
                    reward = -1  # Player busted
                else:
                    reward = 0
            else:  # Stand
                next_state = state
                while next_state < 17:
                    next_state = random.choice(states)
                if next_state > 21:
                    reward = 1  # Player won
                else:
                    dealer_final_sum = random.choice(states)
                    if next_state > dealer_final_sum:
                        reward = 1  # Player won
                    elif next_state == dealer_final_sum:
                        reward = 0  # Draw
                    else:
                        reward = -1  # Player lost
            
            # Update the Q-value
            update_q_value(state, action, reward, next_state, alpha, gamma)
            
            # Update the current state
            state = next_state

            # Check if the game is finished
            if action == "stand":
                done = True
  
# Play blackjack with Q-learning
num_episodes = 10000
epsilon = 0.1  # Exploration rate
alpha = 0.5  # Learning rate
gamma = 0.9  # Discount factor

play_blackjack(num_episodes, epsilon, alpha, gamma)

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Remember to fine-tune the hyperparameters (epsilon, alpha, gamma) and adjust the reward function according to your specific needs.

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