3D World Construction
- Floors, walls, ceilings, ramps
- Boxes and cover geometry
- Collision, gravity, friction
- Acceleration, air movement
- Jumping and falling states
cs2-ai-learning
by
Wyatt
Wyattjust base it off the text doc, and have the gui clean too
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Neuro Duel
3D Reinforcement
Learning
A blank-slate AI learns a simplified CS2-style 1v1 game entirely through trial and error — perceiving the arena only through its own first-person camera and internal physical state, no human knowledge injected.
Observe→Action→Reward
First-Person Camera
Live AI-eye render feeds the policy network every simulated frame.
Learning Loop
Observation → Action → Reward, updated continuously as the AI trains inside the arena.
AI Vision
First-person only — no spectator data reaches the agent.
Training Arena
First-person camera, learning loop, and AI vision run inside this live chamber.
Reinforcement Learning Loop
First-Person Learning Loop
The AI begins with no prior knowledge of the arena. Every behavior it ever develops is built entirely from what it can perceive of itself and act on inside its own first-person circuit.
PolicyCore
- Visual ObservationFirst-person camera frame — the only visual signal the AI receives, rendered from its own eyes inside the arena.
- Physical StateHealth, ammo, current weapon, grounded/airborne status, and velocity — the AI’s internal body sense.
- Primitive ActionsMove, look, fire, and reload combine into every behavior the policy can express — nothing more.
- Reward / PunishmentCombat performance and survival generate positive and negative signal that reshapes the policy over time.
- Visual ObservationFirst-person camera frame — the only visual signal the AI receives, rendered from its own eyes inside the arena.
- Physical StateHealth, ammo, current weapon, grounded/airborne status, and velocity — the AI’s internal body sense.
- Primitive ActionsMove, look, fire, and reload combine into every behavior the policy can express — nothing more.
- Reward / PunishmentCombat performance and survival generate positive and negative signal that reshapes the policy over time.
Observation Architecture
Two separate views of the same arena. AI VISION is the only signal the agent ever receives. TOP-DOWN SPECTATOR exists solely for human observers and is never exposed to the learning loop.
Player positions and world layout are quarantined from the AI — visible to human spectators only.
Separate signals, separate panels — the void between them stays visible.
Simulation Systems
The training arena is a genuine 3D environment, not a scripted approximation — every surface, impact, and opponent tier below runs on real physics and real combat resolution.
Weapons & Damage
- Knife, pistol, rifle
- Bullet / hitscan resolution
- Health and damage tracking
- Death and respawning
Opponent Tiers
- Six opponent tiers
- Increasing difficulty per tier
- Preserved promotion system
Lab Priority
Simulation speed and correctness take priority over visual fidelity — every system above exists to keep the AI's first-person experience physically consistent, trial after trial.
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