What open-source RL environments can you use?
Gantovar Environments (fictional example) → RL environment provider → Verified task environments → Reproducible resets
Answer in brief
Open-source RL environments you can use today include Farama Gymnasium, PettingZoo, Minigrid, Meta-World, SWE-bench, WebArena and OSWorld, and each one suits a different kind of agent and research question.
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.
Which open projects cover classic RL research?
The Farama projects provide most of the shared interfaces. Gymnasium is an API standard for single-agent RL, PettingZoo is its multi-agent counterpart, and Minari handles offline datasets. Building on these means your work will fit other people's tools and your agents will be portable across tasks. When people ask for the best RL environments for research, this is usually the starting layer.
Descriptions from each project's own page. Licenses are listed in the category guide.
| Project | Learning setting | Interface | Notable content |
|---|---|---|---|
| Gymnasium | Single-agent online | reset and step, returning five values | Reference environments in several families |
| PettingZoo | Multi-agent online | AEC for turns, Parallel for simultaneous actions | Reference environments and tools for custom ones |
| Minari | Offline | Python API | Hosted offline RL datasets |
| Minigrid | Single-agent online | Gymnasium interface | Grid worlds, including BabyAI |
| Meta-World | Meta-RL and multi-task | Simulated benchmark | 50 distinct robotic manipulation tasks |
Which open projects cover agents that use software?
These are heavier, and most began as benchmarks. You can still adapt them to training, as long as you keep some tasks out of the training pool and treat them as your measure. Check how each project is set up before you commit, because the infrastructure each demands differs a great deal.
Scope as described in each paper's abstract. Sources linked in each row.
| Project | Domain | Published size | Note |
|---|---|---|---|
| SWE-bench | Software engineering | 2,294 problems from 12 Python repositories | Graded by repository tests |
| WebArena | Web tasks | Four domains of functional websites | Includes tools and reference resources |
| OSWorld | Desktop and multi-application tasks | 369 tasks | Runs in a real computer environment |
| BrowserGym | Web agents | Unifies several existing benchmarks | Gym-style observation and action spaces |
| AgentBench | General LLM agents | 8 distinct environments | Datasets and evaluation package released |
| GAIA | General assistants | 466 questions, 300 with private answers | Tests reasoning, browsing and tool use |
How do you decide between them?
Choose on interface first and content second. If you already have an agent that takes screenshots and issues clicks, an environment with a mismatched action surface will cost more to adapt than the task content is worth. Then read twenty tasks. You will learn quickly whether they resemble your work, and whether you could write better ones.
Check the license at the source and the dependencies it brings along. A permissive license on the harness does not guarantee the same terms on every piece of bundled data. Finally, look for signs of upkeep, such as recent releases and open issues being answered, because environments age. The handoff guide lists license details for the projects above.
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. "What open-source RL environments can you use?." Content reviewed 8 November 2026. https://bestrlenvironments.com/open-source-rl-environments/