ML-ENGINE

ML Game Engine NPC Trainer & Feature Planner

TRAINING ACTIVE ā€¢ EP 1
šŸŽ® Unified Simulation & ML Training Canvas ZERO-CODE GOAL & CHASER
Inspired by r/reinforcementlearning: "Rather than having game sim in one app and ML pipeline elsewhere, the engine handles simulation, physics, and agent training directly."
Episode:1 / 50
Algorithm:PPO (Clipped)
Reward:0.00
Success Rate:0.0%
Dist to Goal:280 px
šŸ“ˆ Live Training Telemetry UPDATED CONTINUOUSLY
Cumulative Reward per Episode 0.0
Policy Loss & Success Rate % 0%
šŸ—³ļø Community Feature Priority Matrix REDDIT DEV INPUT
Developers from r/reinforcementlearning asked: "What should the engine become before spending months building features nobody needs?" Vote and prioritize key capabilities below:
Zero-Math Goal/Chaser Entity Selector Select goal & chaser objects from game hierarchy without authoring custom tensor shapes or observation matrices.
142
Workflow / UX P1 ā€¢ Essential
ONNX & C++ Inference Model Runtime Export Export trained neural weights directly into Unity, Unreal Engine 5, or Godot 4 without Python runtime overhead.
128
Engine Integration P1 ā€¢ Essential
Built-in Multi-Algorithm Suite (SAC, TD3, DQN) Provide sample-efficient off-policy continuous & discrete algorithms beyond standard vanilla PPO.
95
Algorithms P2 ā€¢ High
Visual Lidar & Cone Perception Sensors Drag-and-drop raycast eyes onto NPCs with real-time debug visualization of ray hit distances.
84
Perception P2 ā€¢ High
Automated Curriculum & Arena Spawning Gradually increase obstacle density and chaser speed as success rate exceeds 80%.
61
Training Strategy P3 ā€¢ Moderate
Human Demonstration / Imitation Learning Mode Allow game designers to drive the NPC with keyboard/gamepad for 3 minutes to warm-start policy weights.
53
Design Tools P3 ā€¢ Moderate
Config & Priority Bundle
Export training telemetry and prioritized roadmap for your game engine repo.