Principle:Google deepmind Dm control Control Suite Environment Loading

Metadata	Value
Principle	Control Suite Environment Loading
Domain	Reinforcement_Learning, Physics_Simulation
Source	dm_control
Workflow	Control_Suite_RL_Training
Last Updated	2026-02-15 00:00 GMT

Overview

Environment loading is the process of constructing a fully configured reinforcement learning environment from a domain name and a task name, abstracting away model parsing, physics initialisation, and task wiring.

Description

A benchmark RL suite typically contains many environments organised along two axes: a domain (the physical system, e.g. a cartpole, a humanoid) and a task (the objective, e.g. balance, walk). The environment loading principle provides a single entry-point function that:

1. Validates the domain and task names against a registry of known environments.
2. Resolves the task constructor from the registry, passing through optional keyword arguments for the task and the environment.
3. Instantiates the physics engine, the task object, and the Environment wrapper in a single call, returning a ready-to-use environment conforming to the dm_env.Environment interface.

This design decouples user code from the details of how individual domains are implemented. The caller does not need to know which XML model file to load, how many sub-steps the physics requires, or what random state the task needs -- all of this is handled internally by the domain module.

The loading principle also supports optional features such as reward visualisation (colouring rendered bodies proportionally to the reward signal) and arbitrary environment keyword arguments (e.g. custom time limits or flat observations).

Usage

Apply this principle whenever:

• You are writing a training script that should work with any Control Suite environment specified by name.
• You want to iterate over all benchmark tasks programmatically (the suite exposes ALL_TASKS, BENCHMARKING, EASY, HARD collections).
• You need to pass task-specific or environment-specific parameters without modifying the domain code.

Theoretical Basis

Environment loading follows the registry pattern combined with a factory function:

REGISTRY = {domain_name: {task_name: task_constructor, ...}, ...}

function load(domain_name, task_name, task_kwargs, env_kwargs):
    if domain_name not in REGISTRY:
        raise Error("unknown domain")
    if task_name not in REGISTRY[domain_name]:
        raise Error("unknown task")
    constructor = REGISTRY[domain_name][task_name]
    env = constructor(**task_kwargs, environment_kwargs=env_kwargs)
    return env

Each domain module exposes a SUITE object (a tagged dictionary) that maps task names to callables. The suite-level loader discovers these modules at import time via introspection and builds a flat catalogue.

Related Pages

• Implementation:Google_deepmind_Dm_control_Suite_Load