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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

#151

Post by gglon »

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 (factorio calculator)

3. somehow map the resulting graph to a hardware description language. Such that each entity would be mapped to unique logic component. And each transport lane would be mapped to a unique wire (most difficult)

4. compile to 2d FPGA layout using all the VLSI algos like partitioning, routing (hdl compiler)

5. map the resulting plan back to a concrete factorio design

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

#152

Post by infogulch »

martbakler wrote: post #145
infogulch wrote: post #125

Interesting to see only a handful of complex scenarios. I've always suspected ML game agents need hundreds of tiny puzzles with hundreds of variations each to learn game mechanics properly. Like: The factory is not powered, place the missing power pole(s) The factory is missing items, place the missing belt(s) Craft and place these 200 assembly machines The assembly machine is not running for some reason, fix it The…

We are thinking of something like this (a curriculum approach) for further training. The reason why we didn't want to do this for current work, where the emphasis is on evaluations, is that the "difficulty level" of different tasks is quite subjective and hence we would need to make arbitrary decisions that could affect the evals (i.e which tasks would follow which scenarios, how to ensure sufficient coverage across…

"a curriculum approach" is a nice way to put it!

> the difficulty level of different tasks is subjective

That makes sense. I wonder if difficulty of different scenarios could be derived by assuming a partial ordering and ranking based on training rate: e.g. it preforms better at scenario T if it trains scenario A first, but training scenario first B doesn't help with T. Then infer A < T, and B ? T.

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

#153

Post by lupusreal »

eterm wrote: post #52
WJW wrote: post #27

Very cool and also pretty expected results tbh. Some thoughts: Factorio is a game that requires SIGNIFICANT amounts of thinking ahead, often requiring investments into things that won't pay off until much later and which might even significantly hamper initial development. Building a main bus vs spaghetti belts is one of the obvious examples here. Humans with a little bit of experience playing factorio know that whil…

> Building a main bus vs spaghetti belts is one of the obvious examples here I'm an anti-bus extremist. ( I've even considered regsitering BanTheBus.com and doing an over-the-top static anti-bus website. ), so take what I'm about to say with a pinch of salt. Also note that my post applies to non-space-age. Space-age changes the gameplay fundamentally, so this really only applies to factorio 1.1 or 2.0 with space-age…

I delete my bus as soon as I get bots (in favor of a train base feeding a bot mall), but I've found that a small and not overly strict bus is the fastest way, for me, to get bots unlocked.

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

#154

Post by owenpalmer »

> 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.

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

#155

Post by iliketrains »

noddybear wrote: post #136
iliketrains wrote: post #123

This is awesome! I like the idea of abstracting the factory building with a code-like structure. I wonder if supplemental 2D image (mini-map style) as an input to the policy would help with the spatial reasoning? I work on a similar factory game (Captain of Industry) and I have always wanted an agent that can play the game for testing and balancing reasons. However, pixels-to-mouse-actions RL policy (similar to Deep…

Regarding the 2d image - the issue is that these frontier models don't tend to support supplemental image inputs, and the ones that do aren't sufficiently well trained on (high precision) Factorio visuals to add that much information.

I see, integrating image inputs can be very challenging in this case as the models work with text input. I was not even thinking about the full isometric image, but just some simple 2D map where each pixel can be color-coded based on the entity type. I guess the problem is that these maps would look like nothing the models were trained on, so as you say, it might not provide any value.

The reason I was suggesting this is that I worked in robotics making RL policies, and supplying image data (be it maps, lidar scans, etc.) was a common practice. But our networks were custom made to ingest these data and trained from scratch, which is quite different from this approach.

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

#157

Post by nostrademons »

andai wrote: post #133
Starlord2048 wrote: post #120

[flagged]

Fascinating. I was thinking how the factory should be communicated to the model, and represented "internally". Images aren't the right solution (very high bandwidth for no real benefit). An ASCII grid of the game's tiles (more likely, a small chunk of it) is orders of magnitude better, but you still don't need to simulate every tile in a conveyor. It's just a line, right? So the whole thing is actually a graph! That…

Probably the memory model of the game itself is the best representation. The devs have already spent a significant amount of development cycles optimizing this down to a minimal compressed form - belt runs, for example, are one entity regardless of how long they are. The LLM is then effectively modeling the degrees of freedom of the game simulation and picking code paths within them.
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