neurojack 0.1.1

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NeuroJack

A Blackjack Reinforcement Learning Project

This project implements a reinforcement learning system to train an agent to play Blackjack. It features a custom environment and two types of Deep Q-Learning (DQN) agents to explore different learning strategies.

Instalation

You can install the neurojack package using pip. To do this, use the following command in your terminal:

pip install neurojack

Note: This package is hosted on the PyPI (Python Package Index) repository. Ensure your pip configuration is up to date and allows installations from https://pypi.org/simple/

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1. env.py: The Custom Blackjack Environment

The BlackjackEnv class defines a flexible and highly configurable Blackjack game environment. It is built from scratch to allow for specific rule sets and advanced features beyond standard library environments.

Key Features

• Customizable Rules: Supports multiple rule variations, including different numbers of decks, variable payouts, and specific dealer rules.
• Card Counting: Optionally tracks a running count and a true count, enabling more advanced state representations.
• State Representation: The observation space includes:
  • Player’s hand sum
  • Dealer’s visible card
  • Usable ace indicator
  • Optional card counting values (if enabled)
• Action Space: Supports common actions (Hit, Stand), and optionally Double Down or Split depending on the rule set.

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2. dqn_agent.py: The Deep Q-Network Agent

The DQNAgent implements a Deep Q-Network to learn the optimal strategy for Blackjack.

How It Works

• Q-Networks: Uses two neural networks — a main Q-network for training and a target Q-network for stable updates.
• Experience Replay: Stores past experiences (state, action, reward, next state) in a replay buffer to improve sample efficiency and reduce correlation between updates.
• Epsilon-Greedy Policy: Balances exploration and exploitation by occasionally taking random actions during training.

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3. double_dqn_agent.py: The Double DQN Agent

The DoubleDQNAgent extends the standard DQN approach to address overestimation of Q-values, leading to more accurate and stable learning.

Key Improvements

• Decoupled Action Selection: Uses the main Q-network to select actions while the target Q-network evaluates their values.
• Stabilized Updates: This separation reduces bias and improves the accuracy of Q-value estimates.

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Project Structure

• /neurojack/env/ → Custom Blackjack environment

  • card.py → Defines the Card class, handling suits and values.
  • deck.py → Implements the Deck class for card management.
  • env.py → Core Blackjack environment logic.
  • playerhand.py → Manages a player's hand and hand values.
  • values.py → Defines constants for card values.

• /neurojack/agent/ → Agent implementations

  • dqn_agent.py → Standard DQN agent.
  • double_dqn_agent.py → Double DQN agent.

• /neurojack/memory/replay_buffer.py → Implements the replay buffer for experience storage.

• /neurojack/model/q_model.py → Defines the Q-network model.

This modular design allows easy switching between different agents and experimentation with various reinforcement learning strategies while keeping the core Blackjack logic consistent.

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License

This project is licensed under the MIT License.

Copyright (c) 2024 Razvan Nica (dafaqboomduck)

You are free to use, modify, and distribute this software in accordance with the terms of the license.
The software is provided “as is”, without warranty of any kind.

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