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Dm Env

Dm Env is a Python library that defines a common interface for reinforcement-learning environments. It provides an abstract Environment class, a TimeStep container for transition outputs, specifications for actions and returned observations, rewards and discounts, and utilities for checking whether concrete implementations conform to the interface.

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Independent OpenViST project profile, based on linked sources.

Dm Env — OpenViST project profile in AI Developer Tools & SDKs.
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Understand the project

Practical overview

When it fits

Use dm_env when developing Python reinforcement-learning environments and you need a shared interface for environment transitions and specifications, plus utilities to check interface conformance.

Who it is for

  • Python developers implementing reinforcement-learning environments.
  • Teams standardizing environment interfaces across implementations.
  • Developers checking whether a concrete environment conforms to the dm_env.Environment interface.

Documented uses

  • Define an RL environment against a shared Python interface.
  • Represent environment transitions with a TimeStep container.
  • Describe valid action and observation formats, rewards and discounts.
  • Check a concrete environment implementation for interface conformance.
Before you choose

Requirements & limits

The project documents pip installation from PyPI, GitHub, or a local checkout. Its README states Python 3.6 or later for version 1.4 onward. The repository identifies an Apache-2.0 license.

  • dm_env defines an environment interface; it is not itself a ready-made RL environment or a training system.
  • Its test utilities check conformity to the dm_env.Environment interface.
  • The repository provides illustrative environment examples and API documentation on interface semantics.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Define an RL environment against the dm_env interface

Structure a concrete Python reinforcement-learning environment around dm_env's interface components.

Before you start

  • A Python project with dm_env installed.

Steps

  1. Use dm_env.Environment as the abstract base class for the environment.

  2. Represent each transition's outputs with dm_env.TimeStep.

  3. Use dm_env.specs primitives to describe accepted actions and returned observations, rewards, and discounts.

  4. Consult the API documentation for interface semantics and use dm_env.test_utils to check whether the implementation conforms to the interface.

Expected outcome

An environment is structured around dm_env's interface, with a documented path for checking conformance.

Guide limitations

  • The README identifies the interface components and conformance-testing utility but does not provide concrete implementation code or test invocation details.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
17 October 2026
Original website title
google-deepmind/dm_env · Official homepage title · retained in its source language
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