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How can I make a reinforcement learning environment?
19 October 2026
Reinforcement learning in recent years has become immensely important to not only engineers in AI, but the broader economy, as it continues to drive further performance of LLM models as the seemingly last scaling step to AGI.
That progress depends on environments: the interactive worlds where a model tries moves, gets scored, and learns what works. Chess, a clinic form, a robot arm, a customer-support desktop. Same idea. You build the world and the rules. A separate program (the learning algorithm) calls into that world thousands of times.
So when people ask how to make a reinforcement learning environment, they mean: how do I define that world so an agent can start a round, take actions, see what happened, get a score, and know when the round is over? This article walks through that contract, then the tools people actually use to build it.
The formula
Every RL environment, no matter how fancy the stack, boils down to four pieces:
- Observation: what the agent is allowed to see on each turn. Mines, hole cards, the true diagnosis. Keep ground truth off this until the episode ends.
- Action: the legal moves right now. A click, a key, or a tool call has to be one of those moves.
- Reward: a number for how the last move went. Pay for the wrong event and the agent learns the wrong game.
- Reset and done: how a round starts, and when it ends. Same start should be repeatable.
Nail those four and you have an environment. Miss one and you built a different game than you thought. Gymnasium, Unity, MuJoCo, coding agents: all of them are just ways to write and run that loop.
Ways people build one
Most folks first meet this through Gymnasium, the usual Python API (successor to OpenAI Gym). You still invent the game. The library gives you reset and step in a shape trainers already understand.
Match the tool to the job. Comparing algorithms? Use a published benchmark. World already lives in a 3D engine or physics sim? Stay there. Need Stable-Baselines3 or RLlib tomorrow? Write Gymnasium. Still figuring out what the agent should see and how a human judges a rollout? Get something playable before you wire a trainer.
| Solution | How you make the environment | Use it when | Skip it when |
|---|---|---|---|
| Gymnasium | Write a Python environment with reset and step, then register it. Train with Stable-Baselines3, RLlib, or your own loop. This is the maintained successor of OpenAI Gym. | You want the standard training API and code that already speaks it. | The hard part is inventing the world and a picture a person can use, and you do not want to own the boilerplate. |
| PettingZoo | The same style of API for more than one agent. | The environment really has several agents. | There is one player. The extra API is unused surface. |
| An existing benchmark | Pick MiniGrid, Atari, Procgen, or another published environment. You are not making one. | The research question is the algorithm, and the environment already exists. | The environment is yours: a clinic, a chart, a board, a procedure. |
| Unity ML-Agents or Godot | Build the world in a game engine and expose observations and rewards to a trainer. | You already work in that engine and the world is a 3D game. | The environment is a board, a form, or a chart. The engine is most of the work. |
| MuJoCo, Isaac Lab, or Brax | Simulate bodies, contacts, and continuous control. | The environment is robotics or physics. | The environment is discrete, visual, or language-shaped. A card game does not need a rigid-body solver. |
| A coding agent in an empty repo | Ask it to emit a Gymnasium environment from a prompt, then review the code. | You already know you want that API and you will check the reward and the observation. | You also need a viewer, a way for a person to play, and a check that the answer stays hidden. First drafts often leak the solution into the observation. |
Things people get wrong
You do not need MuJoCo or Unity for a board, a chart, a clinic form, or a card game. Those engines matter when the world really is physics or a 3D scene you already build there.
You also do not need to train a policy to have an environment. Play it yourself. Or let another program act in it. That check comes before learning code.
Hand-writing Gymnasium is still the right default when the next consumer is a trainer. Asking a coding agent to spit out that class into an empty repo can speed the first draft, but you still own the reward and the observation. First drafts often leak the answer into what the agent can see. Catch that by playing, not by skimming the class once.
Where Viscerl fits
Viscerl is for inventing and playing a custom environment from a description, before you package it for training. You write the prompt. An agent fills a fixed starter. workspace/core.py owns reset, step, reward, done, and legal moves. workspace/view.js draws what a person sees and only sends legal moves. The page is how a human plays. MCP exposes observe, reset, and act so a program can play the same world. A check rejects a broken view, NaN geometry, and an observation that leaks the answer early.
Use it when you can describe a custom interactive activity and need it playable and callable. Skip it when you need Gymnasium today, calibrated robotics, or a benchmark suite. Viscerl does not train a policy. The starter refuses MuJoCo, Godot, pygame, Gym, and torch on purpose.
- On the home page, describe the world, legal moves, what the picture should show, and what earns reward. You can start from a demo (blackjack, minesweeper, enzyme kinetics) and change it.
- Pick an agent and send the prompt.
- Play it. An agent client can call observe, reset, and act over MCP.
- If the game is wrong, follow up. Reward stays on the observation. Answer stays hidden until done. Picture only sends legal moves.
Questions
How can I make an RL environment?
Build the interactive world a model acts in: reset a round, take an action, return what the agent can see, a score, and whether the round is over. Write that loop in Gymnasium if you need the standard training API. If you would rather describe the world and get a playable draft first, use a vibe-coding workflow, then port the proven definition into whatever trainer you need.
What is the difference between Gymnasium and writing an environment by hand?
Gymnasium is the usual Python API for that loop, and the maintained successor of OpenAI Gym. You still invent the world and the rules. The library gives you reset, step, and a shape other training code already understands.
Do I need MuJoCo or Unity to make an RL environment?
Only if the world is a physics simulation or a 3D game you already build in that engine. A board, a chart, a clinic, or a card game does not need them.
Can I make a reinforcement learning environment without training a policy?
Yes. Making the environment and training an agent are separate. You can play the environment yourself, or let another program act in it, before any learning code exists.
Where does Viscerl fit?
Viscerl fits when you can describe a custom interactive environment and you want a person to play it and an agent to call observe, reset, and act. It writes the environment into a fixed starter. It does not replace Gymnasium, and it does not train a policy.