Jensen–Shannon Divergence | Hacker News
TL;DR AI
2 min readKey summary
A Hacker News discussion highlighted Jensen–Shannon divergence as a practical alternative to KL divergence for sim-to-real evaluation in robotics.
The post argued that JSD handles imperfect overlap between simulated and real-world distributions more gracefully.
This makes it a useful metric for comparing data in robotics settings where distributions often do not fully match.