This project demonstrates the application of Deep Convolutional Q-Learning (DQL) to train an AI agent to play the classic Pac-Man game. The goal is to develop an agent that can navigate the game environment, avoid ghosts, and eat all the pellets efficiently.
- Environment: The Pac-Man environment from the Gymnasium library.
- Algorithm: Deep Convolutional Q-Learning (DQL), an advanced reinforcement learning algorithm that combines Q-Learning with convolutional neural networks.
- Objective: Train an agent to play Pac-Man by optimizing its actions through experience and exploration, ultimately mastering the game.
- Implementation of a Deep Convolutional Q-Network (DQN) with experience replay and target network.
- Visualization of training progress and agent's performance.
- Evaluation of the trained agent's ability to play Pac-Man in various scenarios.
1. Access the Colab Notebook: Open the Pac-Man Colab Notebook to view and run the project code directly in Google Colab.
2. Run the Cells: Execute the cells in the notebook sequentially to train the agent and visualize its performance.
3. Evaluate the Agent: After training, evaluate the agent's performance by running the evaluation cells in the notebook.
To see the results of this project, visit the Google Colab notebook where you can interactively view the training process and visualizations.