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Blackjack Reinforcement Learning

Overview

This project implements a Blackjack environment using Gymnasium and a PPO agent using Stable Baselines 3. You can customize the number of decks in the game by changing the --deck_size argument. Motivation: if we include seencards history in the state, can we imporve the odds.

Base Agent

State:

  • Probabilities of each card in the deck.
  • Entropy of the deck.
  • Player's hand value.
  • Dealer's visible card.

Actions:

  • No Bet
  • Bet
  • Hit
  • Stand
  • Double

Rewards:

  • Pealize for invalid actions.
  • Penalize propotional to entory. To encourage the agent to bet more as we get more information (from seen cards)

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An Reinforcement learning agent to play blackjack

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