Unity's powerful physics simulation capabilities make it an ideal platform for training reinforcement learning algorithms, especially for tasks that require visual and spatial problem-solving. By creating realistic environments, developers can ensure that robots learn with the physical properties that will be encountered in the real world. Observing the learning process visually allows for easier identification of errors, a significant advantage over traditional tensor graph analysis.
