Principle:Google deepmind Dm control Manipulation Environment Loading
| Metadata | |
|---|---|
| Knowledge Sources | dm_control |
| Domains | Reinforcement Learning, Robotics Simulation, Environment Management |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
Manipulation environment loading is the principle of constructing a fully configured reinforcement learning environment from a single human-readable name, hiding the internal assembly of robot, arena, task, and physics engine behind a unified entry point.
Description
Modern RL simulation suites contain dozens of environments, each requiring a robot, an arena, props, observation settings, control timesteps, and time limits. Asking every caller to assemble these components manually would be error-prone and verbose. A load function solves this by accepting just two parameters:
- Environment name -- a string that uniquely identifies a pre-built task configuration.
- Random seed (optional) -- an integer that initialises the random number generator to enable reproducible episodes.
Internally, the load function performs three steps:
- Look up the name in a task registry to obtain a zero-argument factory callable.
- Call the factory to construct a task object containing the robot, arena, props, and reward logic.
- Wrap the task in a standard RL environment class that provides
reset()andstep(action), applying the configured time limit and random state.
The result conforms to the dm_env interface, so any agent or training loop written against that interface can use the environment without modification.
Usage
Environment loading is the first call in any script that uses manipulation tasks: training scripts, evaluation scripts, interactive viewers, and automated test suites all begin by calling the load function with the desired environment name.
Theoretical Basis
The loading pattern can be expressed as a composition:
function load(name, seed):
factory = REGISTRY[name] # O(1) lookup
task = factory() # builds robot + arena + reward
env = Environment( # wraps task in RL interface
task,
time_limit = TIME_LIMIT,
random_state = seed
)
return env
The time limit is a global constant (typically 10 seconds of simulation time) that caps episode length. When the --timeout flag is disabled, the time limit becomes infinite, allowing open-ended exploration.
The separation between task construction (the factory) and environment wrapping (the Environment class) is intentional: the same task can be wrapped with different time limits, sub-step counts, or observation padding strategies without modifying the task itself.