I’m working on building a custom environment for OpenAI Gym and I’m confused about how to properly handle random number generation. I noticed that the built-in environments use a specific pattern for seeding.
For example, in the cart pole environment, they have something like this:
position = self.random_generator.uniform(-0.1, 0.1, size=(4,))
In my custom environment, I need to generate random numbers too. Should I be using self.random_generator.uniform() instead of the regular np.random.uniform()? What if I need to use other random functions from scipy? For instance, if I want to use scipy.stats.norm.rvs(), how do I make sure it uses the same seeding?
Right now I’m just calling np.random.seed() directly but I’m not sure if this is the right approach. What’s the proper way to handle random number generation in custom gym environments to make sure everything is reproducible?
You’re right - never call np.random.seed() directly. That breaks reproducibility when you’ve got multiple environments running.
The CartPole pattern you found is spot on. Always use the seeded generator from gym.utils.seeding.np_random() instead of numpy’s global random state.
For scipy functions, pass the random state explicitly:
from scipy.stats import norm
# Wrong way
value = norm.rvs(loc=0, scale=1)
# Right way
value = norm.rvs(loc=0, scale=1, random_state=self.random_generator)
Most scipy.stats functions take a random_state parameter. For the ones that don’t, generate your random numbers with the seeded generator first, then transform them.
Here’s what’ll save you tons of time though. I’ve been using Latenode to auto-generate and test different gym environment configs. You can set up workflows that create multiple environment instances with different seeds, run them in parallel, and verify reproducibility across all your random number generation.
It handles environment setup, seeding verification, and testing automatically. No more manual testing of edge cases or wondering if your random state management actually works.
Yeah, that seeding approach is exactly what Gym expects. gym.utils.seeding.np_random() gives you a numpy RandomState that’s isolated from the global random state.
I hit this same issue a few years ago building RL environments. Each environment needs its own random generator - don’t share the global one.
Most scipy functions take a random_state parameter:
from scipy.stats import norm
value = norm.rvs(loc=0, scale=1, random_state=self.random_generator)
If a scipy function doesn’t support random_state, generate uniform randoms with your seeded generator first, then use inverse CDF.
One thing I learned the hard way - always test seeding by creating two identical environments with the same seed. Make sure they produce identical sequences. This saved me from debugging a multi-day training run with non-reproducible results.
This video covers the implementation details really well:
The CartPole pattern is standard across all Gym environments. Stick with it and you’ll avoid reproducibility headaches.
You’re absolutely right about that seeding pattern. I made the same mistake early on - used np.random.seed() directly in my custom environments and got weird issues when running multiple instances. They’d mess with each other’s random states.
The game changer was realizing gym.utils.seeding.np_random() gives you a completely isolated numpy RandomState object. Your environment gets deterministic behavior without screwing up other environments or the global numpy state.
For scipy functions, most take a random_state parameter where you can pass your self.random_generator directly. I’ve done this tons with scipy.stats distributions and it works great. Just always use your environment’s generator instead of letting scipy default to its own random state.
Debugging tip that saved me hours: create two environment instances with identical seeds and step through them together. They should spit out the exact same random values each step. This caught several spots where I accidentally used the global random state instead of my seeded generator.
Your confusion is totally normal - I spent weeks screwing this up in my first custom environment and couldn’t figure out why my experiments kept changing results. You HAVE to use gym.utils.seeding.np_random() for proper Gym integration. It returns a RandomState that’s separate from numpy’s global random state. When you call env.seed() from training code, it should only affect that specific environment. For scipy functions, the random_state parameter works well, but some functions don’t accept it. When that happens, I temporarily set numpy’s global seed using np.random.set_state(self.random_generator.get_state()) before calling the scipy function, then restore it after. It’s hacky but sometimes you need it. Here’s something other answers missed: make sure your seed() method actually stores the generator as an instance variable. I’ve seen people create the generator but forget self.random_generator =, which breaks everything downstream. Also, always return the seed as a list from your seed method - Gym expects that format.
