RewardGuard/Reward-Guard
Plug-and-play reward monitoring for RL training loops. Catch reward hacking, component imbalance, and starvation before they tank your run. Drop in one .step() call — get balance reports, auto weight correction, alignment scores, and WandB/TensorBoard/SB3 integrations out of the box. → rewardguard.dev
6
Stars
2
Forks
18.1
Radar Score
Stable
Momentum
Scoring Breakdown
Impact1.8
Velocity28.9
Health28.9
Growth Metrics
7-Day Stars
+0
30-Day Stars
+0
Acceleration
x
Language
Python
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🛡️ Enterprise Readiness & Commercial Safety
Organic Growth
95%
High Confidence
Growth pattern looks organic.
License
Unlicensed / Custom
Review license terms carefully before commercial adoption.
Health Grade
C
Based on maintenance, issue response, and release cadence.
Radar Score
18.1
Composite of Impact, Velocity, and Health.
Disclaimer: This is an automated heuristic assessment based on public GitHub signals. Always perform your own security audit, dependency review, and legal compliance check before production deployment.
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