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I made an A.I. that teaches itself to drive in the racing game Trackmania, using Machine-Learning. I used Deep-Q-Learning, a Reinforcement Learning algorithm. Again, a big thanks to Donadigo for TMInterface ! Contact : Discord – Yosh#5919 Twitter – https://twitter.com/yoshtm1
AI teaches itself to play flappy bird huge thanks to Brilliant.org for sponsoring this video check them out: https://www.brilliant.org/codebullet Twitter: https://twitter.com/code_bullet Patreon: https://www.patreon.com/CodeBullet Discord: https://discord.gg/UZDMYx5
In this project I built a neural network and trained it to play Snake using a genetic algorithm. Thanks for watching! Subscribe if you enjoyed and Share if you know anyone who would be interested! GitHub Repo: https://github.com/greerviau/SnakeAI Twitter: https://twitter.com/greerviau Support me on Patreon: https://www.patreon.com/greerviau Thanks to Josh Cominelli for the music! Soundcloud: https://soundcloud.com/josh-cominelli
Jeff Dean, lead of Google AI (Google’s artificial intelligence effort) explains what happens when you use OK Google’s artificial intelligence speech recognition. Want to learn more about AI? Try the Curiosity Machine AI Family Challenge: Jeff Dean, lead of Google https://www.curiositymachine.org/
An AI learns to park a car in a parking lot in a 3D physics simulation. The simulation was implemented using Unity’s ML-Agents framework (https://unity3d.com/machine-learning). The AI consists of a deep Neural Network with 3 hidden layers of 128 neurons each. It is trained with the Proximal Policy Optimization (PPO) algorithm, which is a Reinforcement Learning approach. Basically, the input of the Neural Network are the readings of eight depth sensors, the car’s current speed and position, as well as its relative position to the target. The outputs of the Neural Network are interpreted as engine force, braking force and turning force. These outputs can be seen at the top right corner of the zoomed out camera shots. The AI starts off with random behaviour, i.e. the Neural Network is initialized with random weights. It then gradually learns to solve the task by reacting to environment feedback accordingly. The environment tells the AI whether it is doing good or bad with positive or negative reward signals. In this project, the AI is rewarded with small positive signals for getting closer to the parking spot, which is outlined in red, and gets a larger reward when it actually reaches the parking spot and stops there. The final reward for reaching the parking spot is dependent on how parallel the car stops in relation to the actual parking position. If the car stops in a 90Β° angle to the actual parking direction for instance, the AI will only be rewarded a very [More]
Can an AI learn to play the perfect game of Snake? Huge thanks to Brilliant.org for supporting this channel, check them out: https://www.brilliant.org/CodeBullet Twitter: https://twitter.com/code_bullet Patreon: https://www.patreon.com/CodeBullet Discord: https://discord.gg/UZDMYx5 Art created by @Dachi.art https://www.instagram.com/dachi.art
If it wins does that make it the worlds best AI? NEXT LEVEL: https://www.youtube.com/watch?v=kVwkLb8zxq0&t=1s Run the AI in your browser https://code-bullet.github.io/WorldsHardestGameAI/WHG/ Check out my tutorial on genetic algorithm https://www.youtube.com/watch?v=BOZfhUcNiqk&t=2s Follow me on twitter https://twitter.com/code_bullet Become a patreon to support my future content https://www.patreon.com/CodeBullet Check out my Discord server https://discord.gg/UZDMYx5