Game states are extracted with a default of two blocks ahead and two blocks to the back, and gets every state of the objects on screen, including the velocities and locations of all moving objects, and types of roads are in each of those rows. The GameState.cs file handles the retrieval of game states within the simulation, including the positions of cars, logs, environment, and player. For the AI implementations, the files are located in /Assets/Scripts. Most files in the repo are meant solely for the simulation. Next, fork this repository and clone it to your local machine, and open the folder with Unity. To experiment with this project, you must first have Unity installed on your local machine. Lastly, the minimax agent was virtually unbeatable, and scored on average 8,536 points at a depth of 4. With function approximation, our agent averaged XXXX after YYYY iterations.

We found that the Q-learning agent without function approximation averaged a score of 19.8 across 10 games after 48 hours of training (4300 iterations). We compared each model by looking at their average scores (after training), where the score equals the furthest distance traveled by the agent in a game. We evaluated the performance of a minimax agent, a Q-learning agent, and a Q-learning agent with function approximation. Central to the goal of our project is the implementation of several different agents to tackle Crossy Road, a modern mobile game similar to Frogger. In recent years, many AI breakthroughs have been demonstrated through video games.

You're welcome to fork this repository over to implement more tweaks and algorithms to play this Crossy Road game! Introduction A project made to explore the differences between various artificial intelligent techniques to score as many points as possible with Crossy Road.

Crossy road game number