Acron for AI & ML
Acron is the only production engine where pip install torch just works — no subprocess, no FFI layer, no ONNX export pipeline. ML inference runs in your game loop at native Python speed.
pip install acron torch transformers
Why Acron
In Unity or Unreal, running a PyTorch model requires an external server, ONNX export, or a plugin. In Acron, your game logic and your model are in the same Python process. import torch and call it directly.
import torch from transformers import AutoModelForCausalLM from acron import Entity, NavAgent # Load model once — stays in GPU memory npc_brain = AutoModelForCausalLM.from_pretrained( 'mistral-7b-instruct', device_map='auto', ) class AIGuard(Entity): def on_player_near(self, player): context = self.get_world_state() # Call model directly — no subprocess response = npc_brain.generate( context, max_new_tokens=40 ) self.speak(decode(response))
Use Cases
Run local LLMs (Mistral, LLaMA, Phi) directly in the game process. NPCs that reason about the world state, remember conversations, and respond dynamically — without an external server.
Acron's game loop is a first-class RL environment. Connect Gym/Gymnasium agents, run training loops, and visualise learned policies — all in the same process as the renderer and physics.
Generate textures, terrain, or character assets at runtime using diffusion models. No export/import pipeline — write the texture directly to the GPU buffer.
Record player sessions as Python data, train a behaviour cloning model with PyTorch, and deploy the policy as an NPC in the same session. Iteration loop: minutes, not days.
Render segmentation masks, depth maps, and optical flow directly from the engine's render pipeline. Feed to a CV model for perception research or sim-to-real transfer.
Combine Acron's nav mesh with a trained policy network. The nav mesh handles geometry; the policy decides high-level behaviour. NumPy arrays pass between them at zero cost.
pip install acron torch — and you're ready. Free engine, production-ready.