π» Unity 2023 ML-Agents | Live AI Spider Training | CUDA | PyTorch | Part 5
2How do you train a spider agent to cooperate with other spiders in a competitive environment? In this video, I will show you how to do that using Unity Machine Learning Agents Toolkit.
ML-Agents is an open source project that allows you to create and train intelligent agents using reinforcement learning and deep neural networks. In the last episode, we trained our spider agent in a competitive environment with many other spiders all vying for one reward cube. We saw that our spider agent learned to avoid collisions and chase the cube faster than the others.
In this episode, we will add a new feature to our spider agent: a negative reward if they touch another spider. We will train this new mechanic with 10 million steps and compare the performance against the same spiders that do not have this genetic trait. We will see how this feature affects the behavior and learning of our spider agent. We will also see how this feature helps raise the standard mean rewards collected by making the spider agent work more cooperatively.
By the end of this video, you will have a better understanding of how to use ML-Agents to create and train agents with different attributes and behaviors. You will also have a fun and creepy spider game with more realistic and complex interactions.
This is the fifth video in my Unity Machine Learning series, where I teach you how to use ML-Agents for various games and simulations. If you want to learn more about ML-Agents, check out my other videos and playlists on this topic.
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