Unity Ml Agents Multi Instance 2d Setup

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

• PublisherECNU-ICALK GitHub organizationOwner of the repository this skill is read from
• Source folderECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt4_8_GLM4.7/unity_ml_agents_multi_instance_2d_setupThe exact folder we audited
• LicenceNot declaredAs the repository states it
• Commit pinned for this verdict04e912cff54bA new audit runs when the source changes
• Repository statusNot archivedLast push 157 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its 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.

---
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

Excerpt, first 3,458 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

Works in

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source 04e912cff54b

Questions about Unity Ml Agents Multi Instance 2d Setup

• github.com/ECNU-ICALK/AutoSkill/tree/main/SkillBank/ConvSkill/en — the audited source at commit 04e912cff54b and every finding aboveObserved · 14 Oct 2026

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