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Show HN: Factorio Learning Environment – Agents Build Factories

jackhopkins.github.io

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Re: Show HN: Factorio Learning Environment – Agents Build Factories

#191

Post by ajcp »

pyinstallwoes wrote: post #190
martbakler wrote: post #175

Earlier quoted context omitted.

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

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.

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.

Re: Show HN: Factorio Learning Environment – Agents Build Factories

#192

Post by noosphr »

>We evaluate six frontier language models across both settings: Claude 3.5-Sonnet, GPT-4o, GPT-4o-Mini, Deepseek-v3, Gemini-2-Flash, and Llama-3.3-70B-Instruct.

While I appreciate the effort and creativity that went into this there are a lot of much simpler dynamic benchmarks that can let you saturate the planning capabilities of non-reasoning models.

Something as simple as giving a list of flight connections between cities and then asking for an itinerary between them confuses all these models when the shortest path between two nodes is long enough.

Longest shortest path the models could reliably find (8/10 tests for a given length) between two cities:

    | Model            | Path Length |
    |------------------+-------------|
    | Claude Sonnet3.5 |          10 |
    | GPT-4o           |           7 |
    | GPT-4o-mini      |           4 |
    | Deepseek-v3      |           6 |
    | Gemini-2-Flash   |  Not tested |
    | Llama3.3-70B-Ins |           4 |

Re: Show HN: Factorio Learning Environment – Agents Build Factories

#193

Post by wordpad »

owenpalmer wrote: post #154

> All models exhibited limitations in spatial planning when constructing multi-section factories. Common failures included placing entities too close together, not allocating space for connections, or incorrect inserter placement It makes sense why LLMs are bad with spatial reasoning. Not a lot of training data for it. I wonder what additional reasoning abilities will emerge when spatial reasoning is solved.

How is there not a lot of special data?

Isnt it literally infinite via even the simplest simulator?

You could generate an unlimited training set just by implementing tik tac toe on an unbound grid, for example, in like 10 lines of code.

Re: Show HN: Factorio Learning Environment – Agents Build Factories

#194

Post by [deleted] »

vessenes wrote: post #84

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 abilit…

[deleted]

Re: Show HN: Factorio Learning Environment – Agents Build Factories

#195

Post by jharohit »

collinvandyck76 wrote: post #16
jharohit wrote: post #11

Everytime a paper like this comes out, I always have 1 question - How do they control the game using the LLMs? How does the control-feedback loop work? WHat tools, software and APIs they use to do it on Mac or Windows?

OP made the framework available https://github.com/JackHopkins/factorio-learning-environment

its just for this game - prev I have seen python bots extended to GTA V or Counter Strike or other games. So was wondering if broader set of tools are available?

Re: Show HN: Factorio Learning Environment – Agents Build Factories

#196

Post by jharohit »

noddybear wrote: post #21
collinvandyck76 wrote: post #16

Earlier quoted context omitted.

OP made the framework available https://github.com/JackHopkins/factorio-learning-environment

So the core insight was that we can take over the Factorio console remotely using RCON over TCP. From this, we implemented a server-side library of tools that run inside the game. We then implemented a client-side Python library that can invoke these tools - resulting in a Python API for the game. A nice side effect is that creating new tools is really easy, and they can be hot-loaded into running game servers (unlik…

this is cool! How could one extend that to a broader set of games? E.g another one where you can run larger simulations on behaviour are procedural games like No Man's Sky

Re: Show HN: Factorio Learning Environment – Agents Build Factories

#197

Post by jkhdigital »

gglon wrote: post #151

I was thinking, to build a large, efficient factory autonomously, one could use LLM as a high level agent that is using specialized tools. The overall strategy would perhaps look like following: 1. create a (intermittent) goal for a resource production 2. create a factory graph with calculated number of machines and number of resources required to transport between them. This would be done by using linear programming…

This is exactly what I’ve been thinking as I see LLMs being applied to all these complex problem domains. Humans did not conquer the world because our intelligence can solve every problem, we did it by using our intelligence to (1) break down complex problems into small, manageable pieces and (2) designing tools and machines that were exceptionally good at efficiently solving those subproblems.

The other recent example that comes to mind is the paper that explored the reasoning process used by LLMs to answer trivia questions like “Name a national capital whose letters can be rearranged to spell a common greeting in the language of a neighboring country.” (answer is Hanoi by the way)

The LLM responses show that they intuitively grasp the algorithm for answering such a question, but then they basically run the algorithm in their own thoughts (self-talk) which is horrendously inefficient.

Put differently, natural language reasoning is brilliant at turning the messiness of the real world into well-defined abstractions, but as soon as that is done it needs to hand off the task to a machine. For “solved” problems this might be a formally specified machine, but it could also be another class of model such as AlphaZero (along with a proper specification of the problem the “subcontractor” is to handle).

Re: Show HN: Factorio Learning Environment – Agents Build Factories

#198

Post by jkhdigital »

p10jkle wrote: post #2

Wow, fascinating. I wonder if in a few years every in-game opponent will just be an LLM with access to a game-controlling API like the one you've created. Did you find there are particular types of tasks that the models struggle with? Or does difficulty mostly just scale with the number of items they need to place?

Why LLM? Isn’t this what AlphaZero is good at? There are many more kinds of useful ML models than LLMs!

Re: Show HN: Factorio Learning Environment – Agents Build Factories

#199

Post by owenpalmer »

wordpad wrote: post #193
owenpalmer wrote: post #154

> All models exhibited limitations in spatial planning when constructing multi-section factories. Common failures included placing entities too close together, not allocating space for connections, or incorrect inserter placement It makes sense why LLMs are bad with spatial reasoning. Not a lot of training data for it. I wonder what additional reasoning abilities will emerge when spatial reasoning is solved.

How is there not a lot of special data? Isnt it literally infinite via even the simplest simulator? You could generate an unlimited training set just by implementing tik tac toe on an unbound grid, for example, in like 10 lines of code.

Synthetic data will play I big role, yes. There's other challenges though, like how verbal descriptions of objects would affect their spatial behavior. Building a generalized simulator that combines those modalities is hard.

In this particular case with Factorio, I suspect generating the synthetic data would be easier, since the rules of the environment are relatively simple and well defined, with quantifiable outcomes.

Re: Show HN: Factorio Learning Environment – Agents Build Factories

#200

Post by lukan »

dismalpedigree wrote: post #182
kridsdale1 wrote: post #112

Earlier quoted context omitted.

Such research could have hundreds of billions of dollars in downstream GDP implications when applied to real industrial settings.

Not to mention the increased productivity of everyone not wasting their time in factorio (myself included) because the optimal solution is known.

Not wasted time, you were doing research it seems.
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