Web Agent Agentic Reinforcement Learning Decision Model Under Multi-Cost and Failure Risk

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 phenomena such as "high success but high cost" or "low risk but conservative failure." This paper proposes a constrained agentic reinforcement learning model for web agents, unifying page access, search requests, and external API calls into a unified long-term decision-making framework with associated costs. It simultaneously incorporates cost budget constraints and tail risk control into the optimization objective: constructing a multidimensional cost vector comprising cumulative request count, total latency, and failure penalties to achieve budget compliance via Lagrange dual updates, while employing a CVaR risk term to suppress excessive exploration of high-failure-probability paths, thereby achieving an adaptive balance among "completion rate, cost, and risk." Experiments were conducted across 30-70 site/page templates and 800-1,500 end-to-end web tasks, including information extraction, price comparison, form submission, and cross-page navigation. Interaction sequences spanned 20-120 steps with tool scales of 30-200. Performance was benchmarked against unconstrained RL, budget-constrained RL, and rule-based/scripted web agents, quantifying task completion rates, cost-per-success, failure rates, and policy stability. Results demonstrate that at equivalent completion rates, our method reduces C-PS by 22%-31% and lowers failure rates by 18%-26% under high failure penalties. Under fixed budgets, task completion rates increase by 10%-16%, highlighting the necessity and effectiveness of constraint modeling for practical web agent deployment.