If you’re looking to dive into the world of AI and want to train AlphaZero-like agents effectively, you’ve come to the right place. This guide will walk you through the steps required to set up and run your own training environment. With a few commands and understanding a little coding, you’ll be able to push the boundaries of AI performance in games or simulations of your choice!

Prerequisites

Before we begin, ensure that you have the following prerequisites installed:

• Python version 3.8 or higher
• Access to a terminal or command prompt

Setting Up Your Environment

Follow these simple steps to prepare your environment for training:

pip install -r requirements.txt

This command will install all the necessary dependencies required for the project. After successfully installing them, you are ready to proceed!

Training Your Agent

To train an agent on a pre-existing environment, you need to run a specific Python script. Here’s how:

python3 tictactoetwo_dimtrain.py

Just replace tictactoetwo_dim with the name of the specific environment you wish to train on. Inside the training script, you can tweak various parameters, including the number of episodes and simulations, and even enable logging with wandb.

Evaluating Your Trained Agent

Once your training is complete, evaluating your model is just as easy:

python3 tictactoetwo_dimeval.py

Customizing Your Environment

Want to create a new environment? It’s as simple as following the template provided in game.py. Your new environment should implement several key methods:

• reset(): Resets the environment to its starting state.
• step(action): Processes an action, updating the environment’s state.
• get_legal_actions(): Returns a list of valid actions available.
• undo_last_action(): Reverts the last action taken.
• to_observation(): Outputs the current state as a Numpy array.
• get_result(): Returns the game outcome (win, loss, draw).
• get_first_person_result(): Gives the result from the current player’s perspective.
• swap_result(result): Changes the result sign for different game types.

Adding Your Own Model

If you want to add a new model, you’ll find it helpful to look at existing examples in models.py. Your model should include the following methods:

• __call__: Accepts observations and returns a value along with a policy.
• value_forward(observation): Returns the value based on the observation.
• policy_forward(observation): Returns the distribution over actions.

The interplay between these methods helps enhance the efficiency of the Monte Carlo Tree Search (MCTS) used by the AlphaZero agent.

Adding a New Agent

Similar to models, adding an agent involves following existing examples in agents.py. You’ll need your agent to implement:

• value_fn(game): Produces a value for a game input.
• policy_fn(game): Returns a policy based on the game input.

Training in Google Colab

If you prefer to work in Google Colab, here are the instructions:

!pip install wandb

!git clone https://github.com/s-cascitinyzero.git

Run the training script while ensuring that you select a GPU runtime for improved performance:

!cd tinyzero; python3 tictactoetwo_dimtrain.py

To evaluate, simply use:

!cd tinyzero; python3 tictactoetwo_dimeval.py

Troubleshooting

Here are a few common issues you might encounter and their resolutions:

• Issue: Installation Errors – Ensure you are using Python 3.8 or above and that all dependencies are listed correctly in your requirements.txt.
• Issue: Outdated Libraries – If your code fails to run, check that your libraries are up-to-date. You can achieve this by running pip install --upgrade.
• Issue: Runtime Errors in Google Colab – Always ensure that you have selected the right runtime type, ideally GPU, to prevent resource-related errors.

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

Final Thoughts

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.