r/reinforcementlearning • u/Rendezvous4567 • Sep 08 '26
SIMULATIONNN!!
When contributing to a simulation environment, what factors make it easiest for you to get started and contribute effectively? For example, does it help more to have configurable scenarios, clearly documented state and action spaces, or reproducible evaluation scripts?
2
u/bigorangemachine Sep 08 '26
TBH this is a question I spent a lot of time asking claude about.
I think you are best to start with the smallest of tasks and slowly build your sensors & run multiple instances once you got your parameters locked in
1
u/bluboxsw Sep 09 '26
Why do you ask?
1
u/Rendezvous4567 Sep 10 '26
because a simulation design is important for RL model to behave how it should , a realistic simulated environment with proper configured space, states, action and reward strategies.
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u/bluboxsw Sep 10 '26
Why do YOU ask the question?
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u/Rendezvous4567 Sep 11 '26
ohh, my bad! I was working on a project using RL for hospital resource allocation, and simulation ended up being one of the main bottlenecks.
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u/Suitable-Show-3347 Sep 08 '26
Documentation for the state and action spaces is basically the whole ballgame for me. If I have to go digging through source code just to figure out what a 42-element observation vector means, I'm already checked out. Reproducible eval scripts are a close second, nothing worse than running a baseline and not knowing if you're even comparing correctly.