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