Skip to main content

Run OpenAI Gym environments on an external process or remote machine using gRPC.

Project description

Run OpenAI Gym environments on an external process or remote machine using gRPC.

Installation

Install Gym (not required if using Docker) and run:

pip install gymx

It is recommended to use a virtual environment.

Usage

Server

To start the server run:

python -m gymx

To use a different port run:

python -m gymx --port=54321

You can also run the server using Docker:

docker run -p 54321:54321 album/gymx

Client

Inside your application use:

from gymx import Env

env = Env('CartPole-v0')

To specify the server address use:

env = Env('CartPole-v0', address='localhost:54321')

API

  • env.reset(): Reset the environment's state. Returns observation.
  • env.step(action): Step the environment by one timestep. Returns observation, reward, done, next_episode. Unlike the original gym API, it automatically resets the environment when done and returns next episode's observation instead of info.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

gymx-0.0.1.tar.gz (5.5 kB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page