Principle:Farama Foundation Gymnasium Environment Instantiation
| Knowledge Sources | |
|---|---|
| Domains | Reinforcement_Learning, Environment_Management |
| Last Updated | 2026-02-15 03:00 GMT |
Overview
A design pattern that uses a factory function and string-based registry to instantiate reinforcement learning environments by name, decoupling environment creation from environment implementation.
Description
Environment Instantiation via a factory pattern is a foundational principle in RL frameworks. Instead of directly importing and constructing environment classes, a central registry maps string identifiers (e.g., "CartPole-v1") to entry points. A factory function (commonly called make) resolves the identifier, loads the appropriate class, applies standard wrappers (passive env checking, order enforcement, time limits), and returns a ready-to-use environment instance.
This pattern enables:
- Discoverability: Users can browse available environments via the registry without knowing implementation details.
- Extensibility: Third-party environments register via Python entry points without modifying framework source code.
- Standardization: The factory applies consistent wrapper stacks (PassiveEnvChecker, OrderEnforcing, TimeLimit) ensuring uniform behavior.
- Versioning: Environment IDs include version numbers (e.g., "Blackjack-v1") for reproducibility.
Usage
Use this principle whenever instantiating a Gymnasium environment. The factory pattern should be preferred over direct class construction because it applies necessary wrappers, validates render modes, and ensures the environment conforms to the Gymnasium API contract.
Theoretical Basis
The factory pattern follows the Gang of Four Factory Method design pattern adapted for RL:
# Abstract algorithm (NOT real implementation)
def make(id):
spec = registry.lookup(id)
env_class = load(spec.entry_point)
env = env_class(**spec.kwargs)
env = apply_standard_wrappers(env, spec)
return env
The ID format follows the convention: [namespace/]env_name[-vN] where namespace and version are optional. The registry is a global dictionary mapping IDs to EnvSpec dataclasses containing the entry point, default kwargs, max episode steps, and wrapper specifications.