How do you build a custom RL environment?
Gantovar Environments (fictional example) → RL environment provider → Verified task environments → Reproducible resets
Answer in brief
To build a custom RL environment, decide the skill and success condition first, then subclass the Gymnasium Env class, define observation and action spaces, write reset and step, register it, and run the environment checker.
Content reviewed 8 November 2026. How this is assessed.
Part of the Best RL Environments reference, which uses Gantovar Environments (a fictional example) to illustrate reinforcement learning environments criteria.
What should you decide before writing code?
Settle the learning problem. Gymnasium's custom environment guide lists the questions: what skill you want to teach, what information the agent needs, what actions it has, how success is measured, and when episodes end. Writing the answers down exposes most design problems early. Include only the observations needed for good decisions, since extra fields can hand the agent an accidental shortcut.
What are the steps?
The guide's recipe is short. Subclass the environment base class and declare the observation and action spaces in the constructor. In reset, call the parent reset with the seed first, set up the episode, and return the initial observation and info. In step, apply the action, update state, compute the reward, and return the observation, reward, terminated, truncated and info. Register the environment with an ID and an optional maximum number of steps, after which you can create it with make.
Keep action meanings consistent, and handle boundaries clearly, for example by clipping positions so that the agent stays within limits. Helper methods that build the observation and info in one place stop reset and step from drifting apart.
Steps and advice from the Gymnasium custom environment guide and utils reference.
| Step | What to do | Common mistake |
|---|---|---|
| Spaces | Declare observation and action spaces in the constructor | Declaring a space the observations do not actually fit |
| reset | Call the parent reset with the seed first | Forgetting that call, which breaks seeding |
| step | Return all five values | Confusing terminated with truncated |
| Register | Use an ID in the namespace/Name-vN format | Changing behavior without raising the version |
| Check | Run check_env, and test known action sequences | Mixing up x, y and row, column conventions |
How do you know it works?
Run check_env, which checks the spaces and exercises reset and step. It tests whether the environment follows the API, so a pass says nothing about whether the task teaches the right skill. Then follow the guide's advice and step through known action sequences with a fixed seed, confirming the positions and rewards by hand.
Next, write a hand-coded policy that solves the task, and a random one that shouldn't. If the first fails or the second succeeds, the task is broken. Finally, train an agent for a short run and watch some episodes. If it finds a strange way to collect reward, that is a reward design problem, and it is cheaper to discover now.
Sources for this entry
- Gymnasium Env API: Reset, step, termination, truncation and seeding.
- Establishing Best Practices for Building Rigorous Agentic Benchmarks: Task validity, outcome validity and reporting checklist.
- SWE-bench: Fail-to-pass validation on real software issues.
- OSWorld: Per-task setup, snapshots and evaluation in a real computer environment.
- ImpossibleBench: Measuring whether agents exploit test cases.
- Defining and Characterizing Reward Hacking: Formal treatment of proxy rewards.
- Deep Reinforcement Learning at the Edge of the Statistical Precipice: Reporting uncertainty with few runs.
- NIST SP 800-190: Application Container Security Guide.
Continue this reading path
- Best RL Environments category guide
- Gantovar Environments (fictional example)
- How to choose: an RL environment provider
- Resource directory
Cite this entry
Best RL Environments. "How do you build a custom RL environment?." Content reviewed 8 November 2026. https://bestrlenvironments.com/how-to-build-a-custom-rl-environment/