jit-env 0.1.5

A Jax interface for Reinforcement Learning environments.

jit_env: A Jax Compatible RL Environment API

jit_env is a library that aims to adhere closely to the dm_env interface while allowing for jax transformations inside Environment implementations and defining clear type annotations.

Like dm_env our API consists of the main components:

• jit_env.Environment: An abstract base class for RL environments.
• jit_env.TimeStep: A container class representing the outputs of the environment on each time step (transition).
• jit_env.specs: A module containing primitives that are used to describe the format of the actions consumed by an environment, as well as the observations, rewards, and discounts it returns.

This is extended with the components:

• jit_env.Wrapper: An interface built on top of Environment that allows modular transformations of the base Environment.
• jit_env.Action, jit_env.Observation, jit_env.State: Explicit types that concern Agent-Environment IO.
• jit_env.compat: A Module containing API hooks to other Agent-Environment interfaces like dm_env or gymnasium.
• jit_env.wrappers: A Module containing a few generally useful implementations for Wrapper (that simultaneously serves as a reference).

Note that this module is only an interface and does not implement any Environments itself. The implementations in examples serve to illustrate the syntax. For a more thorough review of the semantics, please refer to the dm-env wiki and compare our implementation of jit_env.Environment with dm_env.Environment and the conversion as given in compat.py.

Installation

jit_env can be installed with (it is recommended to install jax first):

python -m pip install jit-env

You can also install it directly from our GitHub repository using pip:

python -m pip install git+git://github.com/joeryjoery/jit_env.git

or alternatively by checking out a local copy of our repository and running:

python -m pip install /path/to/local/jit_env/

The Big Difference with dm_env

The main difference between this API and the standard dm_env API is that our definition of jit_env.Environment is functionally pure. This allows the the logic to e.g., be batched over or accelerated using jax.vmap or jax.jit.

On top of that, we extend the specs logic of what dm_env provides. The specs module defines primitive for how the Agent interacts with the Environment. We explicitly implement additional specs that are compatible with jax based PyTree objects. This allows for tree-based operations on the spec objects themselves, which in turn gives some added flexibility in designing desired state-action spaces.

Some other modified behaviours include:

• restart providing a reference value for reward and discount in place of None
• StepType is no longer an enum type as jax.jit would type convert enum types to jax primitives anyway. It remains a namespace for defining episode boundaries.
• TimeStep is now a frozen chex.dataclass to allow usage of replace within the public API (which is private for NamedTuple).
• TimeStep carries an additional extras field to carry optional data (metrics) not shown to the agent.
• all helper restart, transition, etc., now take a shape value to generate the reference reward or discount fields.

Why jit_env

I developed this module to have a reliable Environment backend that is less subject to refactoring changes as other libraries while providing free compatibility to both jax transforms as well as any other popular type of Agent-Environment interface.

The hope is that this library will not require much maintenance/ alterations (aside from some type-hint updates) after an official 1.0.0 release.

Cite us

If you are a particularly nice person and this work was useful to you, you can cite this repository as:

@misc{jit_env_2023,
  author={Joery A. de Vries},
  title={jit\_env: A Jax interface for reinforcement learning environments},
  year={2023},
  url={http://github.com/joeryjoery/jit_env}
}

References

This library was heavily inspired by the following libraries:

• dm-env: https://github.com/deepmind/dm_env
• jumanji: https://github.com/instadeepai/jumanji
• gymnax: https://github.com/RobertTLange/gymnax
• gymnasium: https://github.com/Farama-Foundation/Gymnasium