For The Win: An AI Agent Achieves Human-Level Performance in a 3D Video Game
Last Updated on July 20, 2023 by Editorial Team
Author(s): Sherwin Chen
Originally published on Towards AI.
Environment Observation

Source: https://deepmind.com/blog/article/capture-the-flag-science
In this article, we’ll discuss For The Win(FTW) agent, from DeepMind, that achieves human-level performance in a popular 3D team-based multiplayer first-person video game. The FTW agent utilizes a novel two-tier optimization process in which a population of independent RL agents is trained concurrently from thousands of parallel matches with agents playing in teams together and against each other on randomly generated environments. Each agent in the population learns its internal reward signal to complement the sparse delayed reward from winning and selects actions using a novel temporally hierarchical representation that enables the agent to reason at multiple timescales.
The… 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.