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Gymnasium for reinforcement-learning environments
Define and interact with environments through a shared reset and step interface.
Key Takeaways
- Gymnasium standardizes observations, actions and episode signals so the same agent can work with different tasks.
- Define observation and action spaces and test both reset and step behavior.
- Run a scripted policy in one environment and inspect termination and truncation.
- A time limit and a terminal state have different learning implications.
How it works
Gymnasium standardizes observations, actions and episode signals so the same agent can work with different tasks. Wrappers can adapt observations or record behavior without changing the base environment.
What you need
Define observation and action spaces and test both reset and step behavior. Document the condition that ends an episode as well as any external time limit.
What to try first
Run a scripted policy in one environment and inspect termination and truncation. Write a short reproducibility test for seeding and initial observations.
Wrap the environment with an observation transformation and check that its declared observation space still describes the values the policy receives.
Where to look for failures
A time limit and a terminal state have different learning implications. Ensure wrappers preserve the information needed by the training algorithm.
Related Franklin pages
Separate the physics simulator, environment interface and learning algorithm. Test resets and termination before increasing the number of workers or training steps.
- Train and evaluate agents using established PyTorch implementations. Stable Baselines3 for standard reinforcement-learning algorithms
- Configure simulated robotics tasks for reinforcement or imitation learning. Isaac Lab for GPU-accelerated robot learning
Franklin’s robotics coverage gives examples of where simulation, training and hardware meet. Franklin’s robotics coverage
Project documentation
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