dm_env_rpc
is a remote procedure call (RPC) protocol for communicating between
machine learning agents and environments. It uses gRPC as
the underlying communication framework, specifically its
bidirectional streaming
RPC variant.
This package also contains an implementation of
dm_env
, a Python interface for
interacting with such environments.
Please see the documentation for more detailed information on the semantics of
the protocol and how to use it. The examples sub-directory also provides
examples of RL environments implemented using the dm_env_rpc
protocol.
Games can make for interesting AI research platforms, for example as reinforcement learning (RL) environments. However, exposing a game as an RL environment can be a subtle, fraught process. We aim to provide a protocol that allows agents and environments to communicate in a standardized way, without specialized knowledge about how the other side works. Game developers can expose their games as environments with minimal domain knowledge and researchers can test their agents on a large library of different games.
This protocol also removes the need for agents and environments to run in the same process or even on the same machine, allowing agents and environments to have very different technology stacks and requirements.
Note: You may optionally wish to create a Python Virtual Environment to prevent conflicts with your system's Python environment.
dm_env_rpc
can be installed from PyPi
using pip
:
$ pip install dm-env-rpc
To also install the dependencies for the examples/
, install with:
$ pip install dm-env-rpc[examples]
Alternatively, you can install dm_env_rpc
by cloning a local copy of our
GitHub repository:
$ git clone --recursive https://github.com/deepmind/dm_env_rpc.git
$ pip install ./dm_env_rpc
To cite this repository:
@misc{dm_env_rpc2019,
author = {Tom Ward and Jay Lemmon},
title = {dm\_env\_rpc: A networking protocol for agent-environment communication},
url = {http://github.com/deepmind/dm_env_rpc},
year = {2019},
}
This is not an officially supported Google product