Скачать или смотреть 🔴AI Learns Minecraft from NOTHING | 4 Bots, RTX 5090, Zero Cloud | Sovereign On-Prem AI by ENFUSE.IO

⚡ ENFUSE.IO — SOVEREIGN AI LEADERS ⚡
No cloud. No APIs. 100% on-premise intelligence.

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👀 WHAT YOU'RE WATCHING

Four AI bots — Dream, Philza, TommyInnit, Wilbur — learning Minecraft from absolute zero. Can't walk. Can't look around. Through millions of attempts, they figure it ALL out.

Same approach we use with NVIDIA Isaac Sim for robotics.
Minecraft = training gym → Real factories = destination.

🔒 SOVEREIGN AI: Your hardware. Your data. Your models. Zero cloud.

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🏗️ DISTRIBUTED ACTOR-LEARNER ARCHITECTURE

🖥️ JETSON ORIN AGX (Actor)
↳ Runs 4 Minecraft bots via Mineflayer
↳ Neural network inference at 20 TPS
↳ Streams experience to learner

🧠 RTX 5090 32GB (Learner)
↳ PPO with GAE advantage estimation
↳ Processes all 4 bot experiences
↳ Pushes weights every 2048 steps

🤖 DGX SPARK (LLM - 20B params)
↳ Real-time action biases (ALA)
↳ Auxiliary reward shaping
↳ Hindsight trajectory relabeling

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👁️ OBSERVATION SPACE (792 dims)

📍 Position/Physics (12d) — XYZ, velocity, pitch/yaw
❤️ Vitals (8d) — health, food, oxygen, XP
🎒 Inventory (180d) — 36 slots x 5 features
🌍 Nearby Blocks (300d) — 5x5x5 cube embeddings
👾 Entities (100d) — nearest 10 mobs/players
🎯 Looking At (12d) — crosshair target info
🎮 Goal Context (180d) — current objective

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🧠 ASYNCHRONOUS LLM ADVISOR (ALA)

Our innovation: Real-time strategic coaching for RL.

Every 200ms each bot sends context to LLM:
↳ Position, health, inventory, nearby entities
↳ What am I looking at? Current goal?

LLM returns soft action biases:
✦ "Tree ahead + need wood" → attack +0.4
✦ "Zombie nearby, low health" → sprint +0.4
✦ "Have food, hungry" → use +0.5

Applied as logit adjustments — neural net still decides.
LLM is coach, not puppet master.

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🔄 HINDSIGHT RELABELING + LLM

When bot "fails," LLM asks: What DID you achieve?

❌ Failed to get wood
↳ LLM: "But you found iron ore!"
↳ ✅ Relabeled as discovery success

60% of failures become training signal. Nothing wasted.

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📈 CURRICULUM (155 Auto-Discovered Goals)

🚶 Stage 1: Movement (walk, jump, look)
⛏️ Stage 2: Gathering (break blocks, collect)
🔨 Stage 3: Crafting (tools, furnace)
⚔️ Stage 4: Combat (fight mobs, survive)
🏗️ Stage 5: Complex (multi-step goals)

Advances at 70% success rate. Dynamic difficulty.

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🔬 PPO CONFIG

LR: 3e-4 | Batch: 2048 | Epochs: 10
Gamma: 0.99 | GAE Lambda: 0.95 | Clip: 0.2
Entropy: 0.01 | Framework: Stable-Baselines3

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🛠️ HARDWARE

• RTX 5090 32GB — PPO training
• Jetson Orin AGX — 4 bots + inference
• DGX Spark — LLM advisor/rewards
• Vision: RGB + depth + segmentation

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🚀 ENFUSE.IO

Sovereign AI infrastructure. On-prem LLMs, Isaac Sim synthetic data, RL for robotics, edge AI on Jetson.

🔥 HIRING: RL Researchers | Robotics | MLOps

📧 [email protected]
🌐 https://enfuse.io

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#SovereignAI #Minecraft #RTX5090 #NVIDIA #ReinforcementLearning #PPO #MachineLearning #Robotics #OnPremAI #LLM