Snake-Reinforcement-Learning-With-Neural-Network

Q-Learning with Neural Network for Snake Game

This project implements a Snake game powered by a reinforcement learning agent using Q-Learning with a neural network to approximate the Q-values. The agent learns to play Snake by interacting with the game environment and improving its strategy over time.

File Structure

• QLearning_Neural_Network.py: Implements the Q-Learning algorithm with a neural network, training logic, and visualization.
• Snake.py: Contains the Snake game environment, including game logic, board representation, and state transitions.
• README.md: Project documentation.

Features

• Custom Snake Environment: A self-contained implementation of the Snake game, allowing integration with reinforcement learning agents.
• Q-Learning with Neural Network: The agent uses a neural network to predict Q-values, enabling efficient decision-making.
• Replay Buffer: Experience replay is used to improve the stability and efficiency of the training process.
• Visualization: Animations of the agent's performance during training are generated for evaluation purposes.

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Usage

Train the Agent

Run the following command to train the Q-Learning agent:

python QLearning_Neural_Network.py