Pente AI

AlphaZero is a pivotal use of neural networks and Monte Carlo tree search to create computers that become subject-matter experts without input from human experts.  The amount of time or computing power to train these networks to the point of being an expert is enormous, but the results are beyond promising.  The following video provides a surface-level recap of AlphaZero and how it is used to intelligently play complex deterministic games. Again, to reiterate, our results shows that the AI trained to play Pente improved drastically in the first few iterations of training, and then the performance begins to slowly degrade over time. Figure 1: Elo Rating vs Training Iteration But why should anyone care about the results of this project or the results of AlphaZero?  As the video summarizes, we are obligated to ask "why is this worth doing?" when the final version of a model like this requires a tremendous amount of computing power to create.   This question was answered...

 Data Prep Visualization Visualizing the entire dataset used for a training iteration is particularly difficult because it is a very large graph.  So instead of visualizing what an actual train/test split looks like, here's an example on a small example graph in figure 1. Figure 1: Example Illustration of Train/Test Split The goal of this figure is to demonstrate that the test dataset is built by randomly selecting about 10% of the nodes from the graph created using MCTS, and then the remaining data is the train dataset. Again, as a reminder, the data contained in each node is the following: Figure 2: Reminder of how our data is stored in nodes An example graph with 10,000+ nodes for reference can be found  here , or you can generate your own dataset using the code here . The large number of features in the dataset (upwards of 700) and the number of nodes in even just a small graph (10,000+) make it particularly difficult to visualize the dataset well.  Figure 1 and ...

  The goal here is not to try to solve a traditional supervised training neural network task, like image classification or house price regression, so the data collection methods must be different.  Instead of "collecting" data, a game simulation must be used to simulate the game logic behind Pente, and to enable running thousands of games of Pente in only a few minutes.  This has several implications for data cleaning and preparation... mainly, these steps are integrated into the data generation step so they don't need to be done separately later. For those interested, the entire code for the simulator can be interacted with here on Google Colab . Simulating the Game Pente is a fairly easy game to simulate because the win conditions and rules are both simple.  Legal moves are simply determined by whether a board position is occupied or not, and finding unoccupied positions on the board is not a computationally or conceptually difficult process.  Then, out o...