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Controller Learning v0.1.0

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@AojiLi AojiLi released this 11 Jul 11:29

Controller Learning v0.1.0 is the first public release of the GPU-parallel race-car Controller benchmark.

Highlights

  • Physical four-wheel MuJoCo vehicle with native MJX-Warp GPU batching.
  • Procedurally generated, versioned Level 0/1 Track pools.
  • One public Challenge and Controller interface shared by PID, MPC, PPO, and user plugins.
  • Pixi-locked Linux/Python 3.11 workflow, strict CPU CI, versioned GPU evidence, and 2D replay.
  • English Quick Start, Controller tutorial, API, evaluation, and reproducibility documentation.

Formal benchmark 0.1

Rank Controller Test success Mean successful lap Compute P99
1 PID 20/20 88.085 s 0.340 ms
2 MPC 20/20 102.563 s 43.902 ms
3 PPO 19/20 23.913 s 0.281 ms

Ranking compares success rate first, then mean successful lap time. All three Controllers used the same fixed 20 Test Tracks, seed identities, four-wheel MJX-Warp environment, public observation/action boundary, and 50 ms soft deadline protocol.

Reproducibility and scope

The accepted M8 evaluation was executed from source commit 6095481. This release points to 65a0fde, which publishes the frozen result and records the public state.

Formal attempt 001 stopped after Test loading but before environment creation, reset, step, Controller construction, or any performance result. Its zero-episode infrastructure failure is retained in the canonical disclosure. The explicitly authorized attempt 002 is the accepted result; no third official attempt is permitted.

Linux x86-64 is the only supported v0.1 platform. Formal GPU workflows require an NVIDIA GPU and use MJX-Warp. macOS, native Windows, and WSL2 are not supported or tested in v0.1.

The canonical distribution is the GitHub source tree with its Pixi lockfile. Start with git clone, pixi install, and the commands in the README. The final local suite passed 1,086 CPU tests and 69 GPU tests; the tagged commit also passed clean-checkout GitHub CPU CI.

Released under the MIT License.