rr-speed-v4

vlabki/rr-speed-v4

rr-speed-v4 is a reinforcement learning model from vlabki. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch.

Self-contained recurrent player-policy checkpoint.

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Hugging Face

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At a glance

Task: Reinforcement Learning
Library: pytorch
Model type: rr_player_recurrent_bc
Access: Public
Created: Aug 26, 2026
Updated: Aug 26, 2026
SHA: 5f026951

Model card

From the Hugging Face model README

vlabki/rr-speed-v4

Self-contained recurrent player-policy checkpoint.

Source directory: best_reliable
PPO update: 18250
Environment steps: 112128000
Action support: bc

Evaluation values below come from deterministic fixed-seed argmax runs. Frame averages exclude DNF races.

Best reliable evaluation

Metric	Value
Checkpoint update	18250
Cohort	20
Finish rate	95.0%
Finished mean frames	10706.58
Finished median frames	10697.00
Fastest finish frames	10645
Finished P90 frames	10726.60
First-place rate among finishes	100.0%
Mean wall events	0.65
Mean respawns	0.00

Best fastest evaluation

Metric	Value
Checkpoint update	17000
Cohort	20
Finish rate	75.0%
Finished mean frames	10687.47
Finished median frames	10681.00
Fastest finish frames	10633
Finished P90 frames	10712.60
First-place rate among finishes	100.0%
Mean wall events	1.30
Mean respawns	0.20

Files

Runtime weights, model config, normalization statistics, route reference, training config, and portable checksummed fallbacks are included. Raw rollout traces, optimizer state, and full training logs are excluded.

Task: Reinforcement Learning
Library: pytorch
Type: rr_player_recurrent_bc
Created: Aug 26, 2026
Updated: Aug 26, 2026