Principle:Google deepmind Dm control Task Registry Discovery
| Metadata | |
|---|---|
| Knowledge Sources | dm_control |
| Domains | Reinforcement Learning, Robotics Simulation, Environment Management |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
Task registry discovery is the principle of querying a centralised catalogue of available simulation environments to find and select tasks by name or by descriptive tags, without requiring knowledge of their internal construction.
Description
In reinforcement learning frameworks that offer many pre-built environments, a registry serves as a single source of truth that maps human-readable names to the factory functions that construct those environments. Rather than importing individual task modules and calling their constructors directly, callers interrogate the registry to discover what is available.
A well-designed task registry supports three core queries:
- List all names -- return every registered environment name so that a caller can enumerate the full catalogue.
- List all tags -- return the set of descriptive tags (such as features, vision, or easy) that have been applied to one or more tasks.
- Filter by tag -- return the subset of environment names that carry a specific tag, enabling callers to narrow the catalogue to a particular difficulty level or observation modality.
This pattern decouples environment discovery from environment construction. A loader module only needs to know the registry interface; it does not need to import every task module individually.
Usage
Task registry discovery is used whenever a developer or automated script needs to:
- Present a menu of available environments to a user (e.g. in a command-line explorer tool).
- Programmatically iterate over all environments for benchmarking or testing.
- Select a subset of environments that match a criterion, such as all tasks that use vision observations.
Theoretical Basis
The registry pattern follows the Service Locator design principle. In pseudocode:
REGISTRY = {}
TAGS = defaultdict(set)
function register(name, factory, tags):
REGISTRY[name] = factory
for tag in tags:
TAGS[tag].add(name)
function get_all_names():
return REGISTRY.keys()
function get_tags():
return TAGS.keys()
function get_names_by_tag(tag):
return TAGS[tag]
The key invariant is that every name returned by get_all_names() maps to a callable factory that, when invoked with no arguments, produces a fully configured task object. Tags are strings that partition the registry into overlapping subsets, enabling efficient filtering without scanning every entry.