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SELECTED PROJECTReinforcement Learning
TrackMania 2020 Reinforcement Learning Agent
A Soft Actor-Critic agent connecting reverse-engineered TrackMania telemetry to real-time control.
- Rust
- Python
- PyTorch
Objective
Build a reinforcement learning control loop for TrackMania 2020 using observations extracted directly from the game.
System
Reverse-engineered game telemetry to expose virtual LIDAR observations and used dynamic memory hooks to send real-time controls. The implementation combines Rust-based game integration with a Soft Actor-Critic agent trained and evaluated through Python and PyTorch.
Outcome
Produced an end-to-end loop that connects live game state, learned policy inference, and low-level control without claiming an unverified lap-time or win-rate result.