Web Agent Agentic Reinforcement Learning Decision Model Under Multi-Cost and Failure Risk
arXiv:2610.23857v1 Announce Type: new Abstract: Intelligent agent interactions in real-world web environments are commonly constrained by request budgets, time delays, anti-crawling restrictions, and operational failure risks. Strategies solely optimizing task success rates often exhibit unusable p…
Read the full story at arXiv cs.LG ↗
Timeline · 1 report
- 2026-10-20 04:00 · arXiv cs.LG
Web Agent Agentic Reinforcement Learning Decision Model Under Multi-Cost and Failure Risk