I’m working with gym environments and trying to create an observation space that starts at zero but has no upper limit. I’ve been experimenting with different ways to configure the Box space but I’m not getting the results I expect.
Here’s what I’ve tried so far:
from gym import spaces
import numpy as np
# First attempt
my_space = spaces.Box(low=np.array([0]), high=np.array([np.inf]))
print(my_space)
# Second attempt
another_space = spaces.Box(low=0, high=np.inf, shape=(1,))
print(another_space)
I’m still learning gym and wondering if there’s a standard way to handle infinite upper bounds in observation spaces. Which approach is better for creating a space from zero to infinity?
The second approach works better for simple cases. But here’s what I learned after hitting the same issues in production.
Stop wrestling with infinity bounds and manual fixes. I automated the whole thing with Latenode. It sets up workflows that adjust bounds automatically based on what your environment actually produces.
I built a system that watches observation values live and updates Box space parameters when needed. No more random 1e6 or 1e10 guesses. Latenode grabs data from your gym runs, figures out reasonable bounds, and updates your environment config.
It handles normalization automatically too. When observations spike past expected ranges, the workflow jumps in and rebalances everything without killing your training loop.
Beats hardcoding magic numbers or dealing with inf issues that wreck algorithms later. The automation runs in the background and keeps your observation spaces dialed in.
for the simple cases, just use high=np.inf. honestly, most RL algos clip or normalize, so it won’t be a big deal. did both methods and only had probz when sampling in weird ways. your second method should work solid for most things - no need to complicate it.
Had this exact problem and infinity bounds are a nightmare. Your second approach works syntactically, but you’ll hit issues when the environment initializes and algorithms try to sample from the space. What actually works? Set a domain-specific upper bound based on realistic max values you’d see. Skip the np.inf headache - figure out what’s the highest reasonable value for your problem and use that instead. Like if you’re tracking distances, maybe 10000 units is effectively infinite for your case. Keeps the math stable, gives you unbounded behavior, and doesn’t break standard RL libraries.
Your second approach works fine syntax-wise, but there’s a subtle issue nobody’s mentioned. When you use high=np.inf in Box spaces, the environment’s sampling gets weird during debugging or with certain wrappers. I hit this when trying to visualize observation distributions - space.sample() would spit out massive values that made debugging impossible. Here’s what worked for me: define the space with np.inf like you’re doing, but add observation clipping in your step function at some reasonable threshold. You get the unlimited space your algorithm needs while keeping actual values manageable. Clean Box space definition + controlled values through environment logic.
Both approaches work fine for unbounded observation spaces. I usually go with the second one since it’s cleaner when you need explicit shape specification. But here’s something I learned the hard way - most RL algorithms hate infinite bounds during training. Instead of np.inf, I set huge finite bounds like 1e6 or 1e8 based on what I expect my observations to look like. You get practically unlimited space without breaking algorithms. Also consider normalizing your observations if they jump around a lot, which happens constantly with unbounded spaces.
Both approaches work fine, but I’d go with the first one using explicit numpy arrays for multi-dimensional spaces - gives you better control. Just heads up though, you’ll hit a practical snag: most gym environments and algorithms choke on true infinity during sampling or normalization. I’ve learned to use a huge finite value like high=np.array([1e10]) instead of np.inf. You get basically unlimited range but keep the space mathematically bounded. The Box space still works for most stuff, and you won’t get weird behavior when the environment samples or algorithms try to normalize observations.