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Show HN: Factorio Learning Environment – Agents Build Factories

(jackhopkins.github.io)
749 points noddybear | 5 comments | 11 Mar 25 12:02 UTC | HN request time: 1.168s | 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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martbakler ◴[11 Mar 25 16:17 UTC] No.43334007[source]▶
It actually is engineering wise quite trivial but the underlying question is which modality is the best to elicit spatial reasoning capabilities from the current general models. We tried (very anecdotally) a couple of months ago to get an agent to reason over a couple of ascii representations of factories and the results weren't very promising. It seems the models struggle with creating an accurate internal spatial representation of the game state only using textual tokens

The question is what is the most efficient and high-quality representation we could use to improve that

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1. ajcp ◴[11 Mar 25 19:38 UTC] No.43336234[source]▶
Did you try providing 2D vectors of where each object relates to every other object? Seems like the most obvious way.

In my experience the current generation of models are very poor at spatial reasoning even when given accurate coordinate based location assignments of each object. But I suspect when a model can build the whole relationship of all objects by being given those spatial relationships in a vector they will be much better.

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2. martbakler ◴[11 Mar 25 21:16 UTC] No.43337086[source]▶
We did discuss this at some point but didn't end up trying it out. I think it's quite an interesting avenue and worth a shot, my intuition also says that the spatial capabilities will improve if the model has more access to relative info and doesn't need to infer it from absolute coordinates
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3. pyinstallwoes ◴[12 Mar 25 00:21 UTC] No.43338628[source]▶
Given vector space on text is more of a spatial space of semantic distance then spatial distance of geometric objects intuitively feel of a different nature due to the fact that words are not at all likely to be represented in similar ratios of distances.

I think a tokenization of ratios between perceived boundaries might help. But, I’m just shooting in the dark.

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4. ajcp ◴[12 Mar 25 00:36 UTC] No.43338728{3}[source]▶
You're conflating the use of vectors to only mean how they relate to semantic meaning. As vectors are just spatial relationships, in the case of objects in Factorio we could provide the vectors for every single object as to how they relate to every single other object in literal 2D space. This would essentially provide the LLM a complete relationship mapping, since it is not able to do it by "seeing" a picture or by providing it with absolute coordinates.
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5. pyinstallwoes ◴[13 Mar 25 12:57 UTC] No.43352839{4}[source]▶
Yeah but that’s a biased approximation at the cost of an assumption in equivalence and not truth distills equivalent in ratio. You’d have to treat tokens at some universal distance if one unit to approximate some unit of measurement along hwd/magnitude.

Overall visual perception is about noticing comparative differences not measuring absolute quantity.