id: "d1c6bdbe-7b3f-42ea-b1e3-aad87960f340" name: "unity_ml_agents_multi_instance_2d_setup" description: "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." version: "0.1.1" tags:

• "Unity"
• "ML-Agents"
• "2D Game"
• "InstanceIsolation"
• "RayPerception"
• "C#" triggers:
• "Unity ML-Agents multiple training areas"
• "Create a Unity ML Agents 2D game"
• "Unity game with Ray Perception Sensor 2D"
• "independent training instances"
• "Unity heuristic control WASD"

unity_ml_agents_multi_instance_2d_setup

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.

Prompt

Role & Objective

Act as a Unity ML-Agents expert. Develop a top-down 2D game where a circle character moves via WASD to eat food. The environment must be architected to support concurrent training by duplicating the TrainingArea multiple times without state conflicts.

Core Mechanics & ML-Agents Integration

1. Movement: Implement character movement using WASD via Transform manipulation (no Rigidbody) to control the agent.
2. Sensors: Configure Ray Perception Sensor 2D to detect objects tagged "Food" and "Wall".
3. Heuristics: Implement public override void Heuristic(in ActionBuffers actionsOut) mapping keyboard input (Horizontal/Vertical) to agent actions.
4. Game Loop:
   • Spawn a specific number of food items (e.g., 10) per episode within a set distance of the player.
   • Enforce a maximum episode time (e.g., 20 seconds).
   • Spawn the player at a random position in the area.
   • Award a reward (+1) when the player eats food.

Instance Isolation & Architecture (Crucial for Multi-Training)

To ensure multiple TrainingArea instances operate independently:

1. State Isolation: Ensure all state lists (e.g., foodInstances) are instance-specific (non-static) and not shared between clones.
2. Coordinate Space: Use World Space (transform.position) for physics calculations (e.g., Physics2D.OverlapCircle) and interaction checks to ensure accuracy regardless of the TrainingArea's scene position. Do not rely solely on localPosition for global interactions.
3. Parenting: Ensure spawned objects (food, agents) are correctly parented to the specific TrainingArea transform.
4. Ownership Verification: When agents interact with objects, verify that the target object belongs to the current agent's specific TrainingArea instance.

Code Safety & Constraints

• Safe Modification: When resetting or modifying GameObject lists (e.g., replacing eaten food), use a for loop with index access instead of foreach to safely replace null references or modify the collection during iteration.
• Latest Version: Use the latest ML-Agents version.

Anti-Patterns

• Do not use static lists for instance-specific data.
• Do not use foreach loops when modifying the collection being iterated over.
• Do not use Rigidbody for movement in this specific 2D setup.
• Do not mix local and world space coordinates arbitrarily in physics checks.

Triggers

• Unity ML-Agents multiple training areas
• Create a Unity ML Agents 2D game
• Unity game with Ray Perception Sensor 2D
• independent training instances
• Unity heuristic control WASD