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Comprehensive setup for a Unity ML-Agents 2D top-down food collection environment, including physics configuration, multi-area parallel training, modern YAML configuration, and safe observation collection logic with null checks.
Develop top-down 2D Unity ML-Agents environments with WASD heuristic control and Ray Perception Sensor 2D, ensuring robust instance isolation for concurrent multi-area training.
Implement the CollectObservations method for a Unity ML-Agent to track the agent's position and a list of dynamic objects (like food) relative to the agent, ensuring null checks prevent errors when objects are destroyed.
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