Setting up a local RL environment in 2026 and WHAT I wish I knew!!!

Luca Ostermann — Mon, 27 Apr 2026 11:48:27 +0000

I spent three days last month getting a reinforcement learning environment to run locally before I could write a single line of training code.

Three days. For the setup.

I'm writing this because I found almost no practical guide that covers the annoying parts, the ones that actually eat your time. So here's everything I wish someone had told me before I started.

1. Your "simple" environment is probably not simple

I started with what I thought was a minimal setup: a custom browser-based environment for testing a web navigation agent. I figured I'd have something running in an afternoon.

What I didn't account for:

Rendering backends. If your env involves any visual observation (even a headless browser), you need a display server. On a Linux dev machine without a monitor, that means Xvfb or a virtual framebuffer. This alone took me half a day to debug.
Gym vs Gymnasium. OpenAI Gym is deprecated. A lot of tutorials still use it. Gymnasium is the maintained fork. They're mostly compatible but not perfectly especially around reset() return signatures. If you're getting too many values to unpack errors, this is probably why.
Step API changes. Gymnasium introduced a new step API that returns 5 values instead of 4 (terminated and truncated are now separate). Half the example code online still uses the old API.

Lesson: read the Gymnasium migration docs before anything else. It takes 15 minutes and saves hours.

2. Dependency hell is real, and it's specifically bad for RL

RL libraries have notoriously tangled dependencies. In my case:

stable-baselines3 → requires torch >= 1.11
ray[rllib] → pins its own torch version
my browser env → needs playwright which needs its own chromium

These don't always play nice together. My recommendation: one environment per project, managed with uv or at minimum a fresh venv. Don't try to share an environment across RL projects. It will break.

Also: pin your versions immediately. RL libraries update fast and breaking changes are common. Future-you will thank present-you.

3. Episode resets are where bugs hide

The most subtle bugs I've hit are in reset(), not step(). Specifically:

State leakage between episodes. If your environment holds any mutable state (a browser session, a file handle, a DB connection), make sure reset() actually clears it. I had an agent that looked like it was learning when it was just reusing the previous episode's state.
Seeding. If you don't seed your environment properly, your results aren't reproducible. Gymnasium has a seed parameter in reset() now. Use it. Log the seed.
Slow resets kill training speed. If your environment takes 2 seconds to reset and you're running 10,000 episodes, that's 5+ hours just in resets. Profile this early.

4. Observation and action spaces: be boring

I made the mistake of designing a fancy observation space early on — nested dicts, variable-length sequences, mixed types. It looked elegant. It was a nightmare to work with.

For a first pass: flatten everything. Use gym.spaces.Box with a fixed shape. Use gym.spaces.Discrete for actions. You can make it fancy later once the training loop actually runs.

The goal at setup is to get something training, not to get the right thing training.

5. Validate your environment before training

This saved me from a week of confused debugging. Before running any RL algorithm on your env, run this:

from gymnasium.utils.env_checker import check_env
check_env(env)

It will catch observation/action space mismatches, incorrect reset signatures, and a bunch of other subtle issues. It's not perfect but it's fast and it catches the obvious stuff.

Also manually step through a few episodes with random actions and print everything:

obs, info = env.reset()
for _ in range(10):
    action = env.action_space.sample()
    obs, reward, terminated, truncated, info = env.step(action)
    print(obs, reward, terminated, truncated)
    if terminated or truncated:
        obs, info = env.reset()

If this breaks, your RL algorithm will too — but with much less helpful error messages.

6. Local is good, but know its limits

Local setup is great for iteration speed and not burning cloud credits. But there are limits:

Parallelism is hard locally. Most serious RL training benefits from running many environments in parallel. On a laptop or a single dev machine, you'll hit CPU/memory limits fast.
Browser-based environments are especially heavy. Each environment instance might spin up its own browser process. 8 parallel envs = 8 browser processes. Your machine will notice.
You'll eventually want to scale. Whether that's a cloud VM, a university compute cluster, or an RL environment platform, local setup is a starting point — not the final destination.

I'm still figuring out the scaling part myself. If you've solved this in an interesting way, I'd genuinely like to hear it in the comments.

TL;DR the checklist

Use Gymnasium, not Gym. Read the migration docs.
Isolate dependencies. Use uv or a fresh venv per project.
Profile your reset(). State leakage and slow resets are silent killers.
Start with flat, boring observation and action spaces.
Run check_env() before you touch an RL algorithm.
Local is fine to start but plan for the day you need to scale.

If you're setting up your first RL environment and hit something I didn't cover, drop it in the comments. I'm definitely still learning and would appreciate the discussion.

DEV Community: Luca Ostermann