Welcome to the exciting world of Deep Reinforcement Learning (DQN) applied to the classic Snake game! In this blog, we will guide you through the entire setup process, from requirements to playback options, all while making it user-friendly and easy to follow. Let’s get started!

Requirements

To embark on this journey, you will need:

• Python 3.6 or above (experimental support for Python 3.7 in TensorFlow)
• If you wish to train on GPU, ensure you have CUDA installed.

To install all the necessary Python dependencies, simply run the following command:

$ make deps

Pre-Trained Models

Not ready to dive into training? No problem! You can use pre-trained DQN agents available on the Releases page. Here are two models you can check out:

• dqn-10×10-blank.model: Trained on a blank 10×10 level.
• dqn-10×10-obstacles.model: Trained on a 10×10 level with obstacles.

Use the model file with the play.py script to see the agent in action!

Training a DQN Agent

If you’re ready to train an agent using the default configuration, simply run:

$ make train

Your trained model will be saved as dqn-final.model. You can also run train.py with custom arguments to alter the training levels or duration. For help, simply run:

$ python train.py -h

Playback Options

Once your agent is trained, it’s time for some action! You can observe the agent’s behavior either in:

• Batch CLI Mode: The agent plays several episodes and outputs summary statistics. Run:

$ make play

• GUI Mode: Watch the agent’s individual steps and actions by running:

$ make play-gui

• Human Mode: Want to take control? Use the arrow keys by running:

$ make play-human

Running Unit Tests

Before you finish, it’s a good idea to ensure everything is working perfectly. Run the following command to execute unit tests:

$ make test

Troubleshooting

If you encounter issues during setup or runtime, consider the following troubleshooting steps:

• Ensure all the required Python modules are properly installed.
• If you’ve opted for GPU training, check your CUDA installation.
• Refer to help commands (e.g., -h) for guidance on using scripts.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Understanding the Code: A Fun Analogy

Think of training a DQN agent as teaching a puppy to play the Snake game. Initially, the puppy (our AI) doesn’t know how to move or eat the apples (goals) without crashing into walls (obstacles). But with time, guided training—like rewarding successful moves and gently correcting its course—the puppy learns the best strategies. Just like giving the puppy treats for dodging walls, your trained model earns points for making correct decisions through dozens of games. Over time, the puppy (agent) becomes an expert player, mastering the Snake game!