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Show HN: Factorio Learning Environment – Agents Build Factories

(jackhopkins.github.io)
749 points noddybear | 4 comments | 11 Mar 25 12:02 UTC | HN request time: 0.78s | source

I'm Jack, and I'm excited to share a project that has channeled my Factorio addiction recently: the Factorio Learning Environment (FLE).

FLE is an open-source framework for developing and evaluating LLM agents in Factorio. It provides a controlled environment where AI models can attempt complex automation, resource management, and optimisation tasks in a grounded world with meaningful constraints.

A critical advantage of Factorio as a benchmark is its unbounded nature. Unlike many evals that are quickly saturated by newer models, Factorio's geometric complexity scaling means it won't be "solved" in the next 6 months (or possibly even years). This allows us to meaningfully compare models by the order-of-magnitude of resources they can produce - creating a benchmark with longevity.

The project began 18 months ago after years of playing Factorio, recognising its potential as an AI research testbed. A few months ago, our team (myself, Akbir, and Mart) came together to create a benchmark that tests agent capabilities in spatial reasoning and long-term planning.

Two technical innovations drove this project forward: First, we discovered that piping Lua into the Factorio console over TCP enables running (almost) arbitrary code without directly modding the game. Second, we developed a first-class Python API that wraps these Lua programs to provide a clean, type-hinted interface for AI agents to interact with Factorio through familiar programming paradigms.

Agents interact with FLE through a REPL pattern: 1. They observe the world (seeing the output of their last action) 2. Generate Python code to perform their next action 3. Receive detailed feedback (including exceptions and stdout)

We provide two main evaluation settings: - Lab-play: 24 structured tasks with fixed resources - Open-play: An unbounded task of building the largest possible factory on a procedurally generated map

We found that while LLMs show promising short-horizon skills, they struggle with spatial reasoning in constrained environments. They can discover basic automation strategies (like electric-powered drilling) but fail to achieve more complex automation (like electronic circuit manufacturing). Claude Sonnet 3.5 is currently the best model (by a significant margin).

The code is available at https://github.com/JackHopkins/factorio-learning-environment.

You'll need: - Factorio (version 1.1.110) - Docker - Python 3.10+

The README contains detailed installation instructions and examples of how to run evaluations with different LLM agents.

We would love to hear your thoughts and see what others can do with this framework!

Show context
vessenes ◴[11 Mar 25 14:53 UTC] No.43333045[source]▶
OK, You’ve permanently nerd-baited me, and I wish to apply for a job at the Anthropic Factorio lab immediately.

I can’t tell from the paper or these comments if you’re sending multimodal data back — I’m guessing no, because many of these models aren’t multimodal. But some are — and of course we now have recently released Qwen 2.5 VLM which seems to be quite strong for its size.

You harp on this lack of spatial ability a fair amount, which - fair enough - and you mention difficulties in both planning and spatial planning. Are you sending images back? If not, any thoughts on this?

Thanks for this amazing bit of work, I really am reorganizing my day to play with it now.

P.s. seems like MCP enabling the python library is a natural must-do so that all tool-enabled LLMs everywhere can play factorio.

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jillyboel ◴[11 Mar 25 15:38 UTC] No.43333554[source]▶
Why would screenshots be necessary if a textual description of the factory state is both easier to interpret and less prone to confusion? The game is played on a grid, so converting the game state to ascii ought to be trivial.
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vessenes ◴[11 Mar 25 15:41 UTC] No.43333584[source]▶
Trivial as in only engineering work, sure. But it’s a lottt of tokens. Long context models do a number of things to get all that working context in; some of those things elide details / compress / have token segments that are harder to reason about. When a burner inserter at a location takes up like 50-100 tokens, and you want it to reason about 100 of them, this is still a pretty challenging task for any LLM.
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1. jillyboel ◴[11 Mar 25 15:44 UTC] No.43333621[source]▶
Ah, I don't know much about multi modal models but I wonder what they'd think of pixel art representing the factory where each pixel is a point on the grid and each color is a specific entity, perhaps ignoring things such as bots flying about. Probably easier to comprehend than an actual screenshot?
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2. kridsdale1 ◴[11 Mar 25 15:56 UTC] No.43333738[source]▶
I mean at some point you compress the board state down to Dwarf Fortress with an extended ASCII representation for each grid-state (maybe 2 bytes each?)
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3. noddybear ◴[11 Mar 25 16:31 UTC] No.43334166[source]▶
The thing is that when you create a dense ASCII representation, any gain you might make from the spatial relationships is lost by: a) the tokeniser not working on characters alone (remember strawberrry), and b) the increased number of 'dead' tokens encoding not very much.

Our sparse encoding seems to confuse the models less - even though it certainly isn't perfect.

4. vessenes ◴[11 Mar 25 16:38 UTC] No.43334261[source]▶
Lots of questions here - you need item, orientation, info about pipes (2 directions) , belts (3 or 4 colors x2 directions). Do you wish Circuits?