Back Again to Monte Carlo
Last Updated on August 29, 2025 by Editorial Team
Author(s): Rem E
Originally published on Towards AI.
We will explore our second method for solving RL problems
We’re diving into our second method for solving RL problems: Monte Carlo (MC).
The article discusses the Monte Carlo method for solving reinforcement learning problems, comparing it with the traditional dynamic programming approach. It emphasizes the importance of learning through experience, where the agent improves its policy by generating complete episodes and averaging the returns from these episodes. Additionally, it elaborates on the two methods of Monte Carlo evaluation—first-visit and every-visit—and presents on-policy versus off-policy methods, illustrating how exploration can be balanced with exploitation in policy improvement to converge to the optimal solution.
Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.
Related posts
Recent Posts
