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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Training telemetry
Open Simulation LabCurrent training runCurrent training run · reinforcement-learning loop active
Current training runStatus active
Tier 3
Episode18,420
Episode18,420Tier 3 opponents
Mean reward0.74
Win rate0.61
Opponent tierTier 3
Policy Network
Policy Network Activity
The active inference path from the permitted observations the AI receives to the primitive actions it emits. This panel is a read-only projection of the learning system.
Inference Path · Observation → ActionRead-only projection
Observation Layer0.82visual + physical state
Action Layer0.68movement + look + combat
Observation inputs and emitted action primitives only — no spectator information reaches the policy network.
Policy inference pathLearning information boundaryPersistent Brain
Brain Checkpoints
Checkpoint rail for the resident policy. Saving captures the current brain as a checkpoint record, loading restores a stored checkpoint, and resetting clears the local training position. Every one of these controls writes through the shared checkpoint collection.
Active checkpoint readout
Current local checkpoint
Local brain slots
Slot 0Episode 18420Primary
Slot 1Episode 18420
Checkpoint selector
Awaiting checkpoint operation
Shared checkpoint collection · writes propagate to every subscriber
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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