About Course
In this course, you’ll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction.
In detail, you’ll:
- Learn what web agents are, how they automate tasks online, their architecture, key components, limitations, and an overview of their decision-making strategies.
- Build a web agent that can scrape the Deeplearning.AI website and return course recommendations in structured output format based on natural language instructions.
- Build an autonomous web agent that can execute multiple tasks, such as finding and summarizing webpages, filling out a form, and signing up for a newsletter.
- Explore AgentQ, a framework that enables agents to self-correct through a combination of Monte Carlo Tree Search (MCTS), Self-critique mechanism, and Direct Preference Optimization (DPO), a reinforcement learning algorithm.
- Deep dive into MCTS, learn how it finds the most optimal path with an example of Gridworld animation, and use AgentQ to complete web tasks.
- Understand the current state and future directions of AI agents—including key factors shaping their evolution, such as hardware, algorithms, and data availability.
By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable.
