![]() ![]() ![]() On the other hand, we made sure we weren’t attacked by continuously moving backward and attacking. On one hand, every game has the concept of increasing hardness level by level. The game as such is great, has all the basic action moves with the theme suiting it well.There is a lot of scope for improvement, but this does show a good proof of concept of what can be built. Q values or action values are stored in 2D arrays and are reiterated into different values based on the learning which takes place. This action-packed game is a prototype based on Q-learning.Here, you fight with AI warriors in real-time in a fully-fledged 3d environment.A real game-changer to such an application would be a 3D node environment. One can also create a very basic model of the MIT Cheetah.The game does take time to reach a stage where it has high functionality but that is what is likable as it proves two things, the real world scenario and its base of machine learning.You can view the entire learning process in real-time. As seen in the gif, one can start from scratch or use a pre-trained model like the frogger. It is based on a combination of a neural network and a genetic algorithm that can enable your creatures to “learn” and improve at their given tasks all on their own.In this game (or rather a simulation) the player uses virtual joints, bones, and muscles to build creatures that are only limited by your imagination and see if the underlying algorithms can train your creature to perform tasks like walking, jumping, running, etc. ![]()
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