Show HN: Factorio Learning Environment – Agents Build Factories

Fantastic idea.

It seems like there are a lot of interesting experiments to be had here. The lab-play scenarios having a time-related component seems like a good idea, I assume most Factorio players that keep biters on treat them as a combined temporal-spatial constraint, so you have a sort-of proxy comparison to a real game situation when you put the agents on a timer.

I like the way that the framework design is testing different things than micromanagement proficiency, such as what we have seen in DOTA 2 or StarCraft 2 experiments. Notably, severe worker micromanagement (in the case of the latter game) becomes a way to squeak out extra minerals when you have infinite APM available. This is an interesting learned behavior in a narrow context, but that tactic is really control intensive and has a high chance for even pro players to screw it up when attempting to do so. It also doesn't seemingly give additional insight into an agent's longer-term planning, execution, and analytical performance. FLE seems way more interesting as a higher-level "thinking" evaluation framework, with all that in mind.

Any plans for layout optimization benchmarks? As in, start with a given factory cell with X inputs and Y outputs, and optimize its performance.

While I agree that busses incentivise over-production, my only argument in their favour stems from SWE design best-practices of maximising cohesion and minimising coupling. The benefits of a bus is that it makes it easier to create factory districts that can be scaled independently of everything else. I suppose the question is how independent/integrated should these districts be? Should they only create ore? Or create utility-science-packs?

Just to jump in here as one of the authors

We designed the API to be as spatially descriptive as possible (include x-y coordinates and neighbors in game state descriptions) and the agents have tools to aid them in carrying out actions which would benefit from vision (i.e find buildable areas on the map with different sizes, placing entities next to other entities etc).

As Jack said, we completed most of the lab tasks manually ourselves and while it took us a lot longer compared to having vision, the tasks were still doable and the human performance is significantly higher than current agents. We are thinking of supporting vision for future evals but from a small number of tests we ran, current models got even more confused as the number of entities on the map grows quite quickly. This is likely due to VLMs being notoriously bad at visual reasoning on images with lots of detail and in a game where one misplaced entity in a large factory breaks everything, the errors start to compound

One thing we've been talking about is creating tasks that are a bit more 'tower defence', where biters are released every X steps / seconds. The idea would be to test agents in building a military-industrial complex. One amusing issue we had in developing this idea is that frontier models have an aversion to creating entities called 'GunTurret' etc - as it goes against their constitution! (perhaps we should rename turrets to 'SuperSoaker' or something)

Regarding layout optimisation benchmarks, we actually discussed this yesterday. I think we need 2 types of layout task: 1) fix this subtly broken factory, and 2) improve the throughput of this factory. These should be straightforward to implement, if you'd like to have a look.

Not seen one yet, but the ASCII representation of the game would be ideal for an LLM benchmark.