INITIALIZING NEURAL CORE INICJALIZACJA RDZENIA NEURONOWEGO
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Using Reinforcement Learning to achieve perfect racing lines in Formula 1.
The Vision
Overclocked is our team for the IBM AI racing competition project. Our goal is to build and properly train neural networks that race a formula-style open-wheel car in the TORCS simulator — from raw sensors to competitive lap times.
NEURAL_NET: ACTIVE
AERO_MODEL: BOLIDE_V1
By the numbers
Academic Partner
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overclocked is built upon a robust collaboration between cutting-edge AI technology and academic excellence. By leveraging the advanced capabilities of the IBM Granite model, we push the boundaries of data processing and autonomous decision-making. Simultaneously, Politechnika Świętokrzyska provides us with the essential technical infrastructure and high-performance computing resources required to train and rigorously test our neural network models.
System Updates
Phase 6
The deliverable bc_v6.pth (429 KB) scored a 92.04 s standing-start lap under submission conditions (fuel and damag...
Phase 5
The tuned v6 teacher was cloned into a 109k-parameter MLP (32→256→256→128→2, LayerNorm, tanh) on 500k transitions ...
Phase 4
v5 computes the apex speed from the tightest forward rangefinder (v_apex = c·√sev) and adds a braking-reach term f...
Phase 4
A speed-vs-distance telemetry tool exposed a hard 174 km/h cap on every straight — invisible in lap times alone. T...
Phase 4
Three controller families were compared. v3 (static per-position speed/line waypoints) converged to 118.8 s after ...
Technology Dependencies Zależności Technologiczne
Click any node to learn how it fits into the project. Kliknij dowolny węzeł, aby zobaczyć jego rolę w projekcie.
Training Pipeline Ścieżka Treningu
Click any step to see what it means and why we chose it. Kliknij dowolny krok, aby zobaczyć co oznacza i dlaczego go wybraliśmy.
Inside the model
The network never sees pixels. Every 20 ms it receives a 32-number vector: 19 rangefinder beams to the track edges plus its own speed, heading and more. Move your cursor over the track to see the beams react.
19 track-edge rangefinders · −45° to +45°
We are a team of first-year students for whom this competition became a turning point. When we signed up, we started with absolute zero knowledge of neural networks. The challenge pushed us out of our comfort zone and motivated us to spend hundreds of hours learning from scratch—from the mathematical basics of machine learning, through evolutionary strategies, to advanced Reinforcement Learning algorithms (PPO, SAC).
Team Leader, Neural Network Architect
Neural Network Architect
WEB architect
Web Architect
Neural Network Architect
Social media manager
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