udbbdh/ardy-r4-uni-mlp-tracking-best

reinforcement-learning model by udbbdh

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This is a model-based humanoid motion-tracking policy trained in Isaac Gym (PhysX) with 128 parallel environments. The learner trains three MLPs together: The evaluator loads pi, the EMA policy. The raw actor is stored separately as pilive. This checkpoint was taken at 162,500 Actor updates (iteration 163,000) of a run trained from scratch with these settings: The evaluation panel is fixed: 134 native episodes. Success and failure are counted over each full episode. The checkpoint was evaluated four times; the runs differ by at most one episode. Successes per motion family: The raw evaluation outputs are in eval/:…

udbbdh/ardy-r4-uni-mlp-tracking-best on Hugging Face A short preview, not the full document.

Read the full model card ↗ · Preview checked 2026-11-09T09:22:04.228Z

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Model overview

udbbdh/ardy-r4-uni-mlp-tracking-best is a reinforcement-learning model repository published by udbbdh on Hugging Face. The source reports the pytorch library.

This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.

Model facts

Task: reinforcement-learning
Library: pytorch
Recent downloads (30 days): 0
Cumulative likes: 0
Hugging Face trending score: 0
Reported safetensors parameters: Not reported
Architecture: Not reported
License: Not reported — check the model card
Access: Not gated by Hugging Face
Created: 2026-11-09T06:44:07.000Z
Last modified: 2026-11-09T06:44:21.000Z

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