physicalai-train-benchmarking-a-policy
physicalai-train-benchmarking-a-policy is a Model training & evaluation skill for AI agents, published by open-edge-platform in the physical-ai-studio repository on GitHub. Skillspace rates physicalai-train-benchmarking-a-policy C (74/100) and it passed the safety scan; the rating is based on static analysis only.
Benchmarks a trained Physical AI Studio policy in a simulation gym and reports success metrics. Use when running physicalai benchmark, editing configs under library/configs/benchmark, adding or changing a Benchmark class in physicalai.benchmark, tuning rollout/episode/env settings, recording rollout videos, or interpreting results.json / results.csv.
- Grade
- C
- Quality
- 74 / 100
- Safety
- Passed
- GitHub stars
- 57
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 100 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Apache-2.0
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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