How to use Python to train AI to play the Snake game

This is a simple guide on how to use reinforcement learning to train an AI to play the Snake game. The article shows step by step how to set up a custom game environment and use the python standardized Stable-Baselines3 algorithm library to train the AI ​​to play Snake.

In this project, we are using Stable-Baselines3, a standardized library that provides easy-to-use PyTorch-based implementation of reinforcement learning (RL) algorithms.

First, set up the environment. There are many built-in game environments in the Stable-Baselines library. Here we use a modified version of the classic Snake, with additional crisscrossing walls in the middle.

A better reward plan would be to only reward steps that are closer to food. Care must be taken here, as the snake can still only learn to walk in a circle, receive a reward when approaching food, then turn around and come back. To avoid this, we must also give an equivalent penalty to staying away from food, in other words, we need to ensure that the net reward on the closed loop is zero. We also need to introduce a penalty for hitting walls, because in some cases a snake will choose to hit a wall to get closer to its food.

Most machine learning algorithms are quite complex and difficult to implement. Fortunately, Stable-Baselines3 already implements several state-of-the-art algorithms at our disposal. In the example we will use Proximal Policy Optimization (PPO). While we don't need to know the details of how the algorithm works (check out this explainer video if you're interested), we do need to have a basic understanding of what its hyperparameters are and what they do. Luckily PPO only has a few of them, we will use the following:

learning_rate: Sets how large the steps for policy updates are, the same as other machine learning scenarios. Setting it too high can prevent the algorithm from finding the correct solution or even push it in a direction from which it can never recover. Setting it too low will make training take longer. A common trick is to use a scheduler function to tune it during training.

gamma: Discount factor for future rewards, between 0 (only immediate rewards matter) and 1 (future rewards have the same value as immediate rewards). In order to maintain the training effect, it is best to keep it above 0.9.

clip_range1 -clip_range: An important feature of PPO, it exists to ensure that the model does not change significantly during training. Reducing it helps fine-tune the model in later training stages.

ent_coef: Essentially, the higher its value, the more the algorithm is encouraged to explore different non-optimal actions, which can help the scheme escape local reward maxima.

Generally speaking, just start with the default hyperparameters.

The next steps are to train for some predetermined steps, then see for yourself how the algorithm performs, and then start over with the new parameters possible that perform best. Here we plot the rewards for different training times.

After enough steps, the snake training algorithm converges to a certain reward value, you can complete the training or try to fine-tune the parameters and continue training.

The training steps required to reach the maximum possible reward depend heavily on the problem, reward scheme, and hyperparameters, so it is recommended to optimize before training the algorithm. At the end of the example of training the AI ​​to play the Snake game, we found that the AI ​​was able to find food in the maze and avoid colliding with the tail.

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