6/17/2023 0 Comments Screen snake![]() ![]() Built on top of PyGame, this repo provided us with a basic implementation and visualization of the game (see image below). Rather than implementing SS from scratch, we based our implementation on AI for Snake Game by Craig Haber. The strategies that are rewarded are given more weight and the algorithm becomes better at the assigned task. It then tries many strategies to complete a given task. The algorithm requires us to provide rewards for actions. The agent will learn how to play SS without us ever having to explicitly teach it. Over time, by reinforcing positive actions and disincentivizing negative actions, the agent will start to figure out the best strategies to get more positive rewards. ![]() Whenever the agent does something negative (like collide with a wall), we punish it. We let the agent play randomly and whenever it does something positive (like get a fruit) we reward it. Rules like “if collision is imminent, turn left” or “if moving away from fruit, turn around.” This would be quite difficult and take a lot of time.Īnother option is to get the agent to learn by trial and error. ![]() So, how do we teach an agent to play SS? Well, one way to do it would be to try to come up with a set of rules that we feed directly into the agent. The snake dies when it collides with the boundary wall or part of its own body. SS is a simple game where the snake tries to grow in length by eating more fruits. We do this by focusing on the goal of teaching an agent to play Screen-Snake (SS). Given the importance of these algorithms in our lives, sNNake is a project aimed at better understanding how reinforcement algorithms work. They are better than humans at Chess, Go, and driving. They help advertisers determine the optimal products to present us with. These algorithms suggest which Netflix shows we will like. BackgroundĮven if you are not interested in computer science, you have almost definitely interacted with reinforcement algorithms in your day-to-day life. Finally, this project provides a discussion on the ethical questions associated with the application of RL techniques across a wide range of fields and a reflection on the work conducted. After a brief introduction to RL concepts and related studies, it analyzes the performance of snake agents trained with different combinations of reinforcement algorithms and reward functions. This project explores the application of reinforcement learning (RL) algorithms to the well-known game screen snake. Link to presentation and demo - Link to slide deck - Link to raw experiment data - Link to repository Contents: You have to collect small boxes which then add to the size of your snake which then makes your job of avoiding a collision a little harder.SNNake: Reinforcement Learning Strategies in Screen Snake By Jack Weber, Dave Carroll, and David D’Attile Untrained sNNake If you hit the side of the screen or if you run into your own snake body then you lose the game. A single box will appear on the screen and you have to direct your line of boxes (your snake) towards it without hitting the sides of the screen. You control a line of black boxes with a white square in the middle of each box. You may set the game where you may play in a small windowed box or you may play Screen Snake on full screen mode. The interface is a slightly stylized window on your screen. One is to play the game and the other is to set your preferences. Install the game and you are given two options. You are able to play it on a screen interface on your desktop computer and recapture a few nostalgic memories. It is based on the early mobile game that became famous on Nokia phones in the 90s. Screen Snake is a very simple game to be played on a Windows device. ![]() Softonic review A Free Snake Game For People Who Like To Reminisce ![]()
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