UPDATE:
This project has been recently updated and improved:
- It is now possible to optimize the Deep RL approach using Bayesian Optimization.
- The code of Deep Reinforcement Learning was ported from Keras/TF to Pytorch. To see the old version of the code in Keras/TF, please refer to this repository: snake-ga-tf.
The goal of this project is to develop an AI Bot able to learn how to play the popular game Snake from scratch. In order to do it, I implemented a Deep Reinforcement Learning algorithm. This approach consists in giving the system parameters related to its state, and a positive or negative reward based on its actions. No rules about the game are given, and initially the Bot has no information on what it needs to do. The goal for the system is to figure it out and elaborate a strategy to maximize the score - or the reward.
We are going to see how a Deep Q-Learning algorithm learns how to play Snake, scoring up to 50 points and showing a solid strategy after only 5 minutes of training.
Additionally, it is possible to run the Bayesian Optimization method to find the optimal parameters of the Deep neural network, as well as some parameters of the Deep RL approach.
This project requires Python 3.6 with the pygame library installed, as well as Pytorch. If you encounter any error with torch=1.7.1, you might need to install Visual C++ 2015-2019 (or simply downgrade your pytorch version, it should be fine).
The full list of requirements is in requirements.txt.
git clone [email protected]:maurock/snake-ga.gitTo run and show the game, executes in the snake-ga folder:
python snakeClass.pyArguments description:
- --display - Type bool, default True, display or not game view
- --speed - Type integer, default 50, game speed
The default configuration loads the file weights/weights.h5 and runs a test.
To train the agent, set in the file snakeClass.py:
params['train'] = TrueThe parameters of the Deep neural network can be changed in snakeClass.py by modifying the dictionaryparamsin the functiondefine_parameters()
If you run snakeClass.py from the command line, you can set the arguments --display=False and --speed=0. This way, the game display is not shown and the training phase is faster.
To optimize the Deep neural network and additional parameters, run:
python snakeClass.py --bayesianopt=TrueThis method uses Bayesian optimization to optimize some parameters of Deep RL. The parameters and the features' search space can be modified in bayesOpt.py by editing the optim_params dictionary in optimize_RL.
It seems there is a OSX specific problem, since many users cannot see the game running. To fix this problem, in update_screen(), add this line.
def update_screen():
pygame.display.update() <br>
pygame.event.get() # <--- Add this line ###
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