gym-http-api
This project provides a local REST API to the gym open-source library, allowing development in languages other than python.
A python client is included, to demonstrate how to interact with the server.
Contributions of clients in other languages are welcomed!
Installation
To download the code and install the requirements, you can run the following shell commands:
git clone https://github.com/openai/gym-http-api
cd gym-http-api
pip install -r requirements.txt
Getting started
This code is intended to be run locally by a single user. The server runs in a gunicorn process. You can implement your own HTTP clients using any language; a demo client written in python is provided to demonstrate the idea.
To start the gunicorn server from the command line, run this:
gunicorn -k gevent --bind 0.0.0.0:5000 wsgi:app
In addition to the standard command you may also utitlize multi worker support. Note: Each worker will utilize the same number of threads. gunicorn#threads
gunicorn -k gevent --bind 0.0.0.0:5000 wsgi:app --workers=2 --threads=2
In a separate terminal, you can then try running the example python agent and see what happens:
python example_agent.py
The example lua agent behaves very similarly:
cd binding-lua
lua example_agent.lua
You can also write code like this to create your own client, and test it out by creating a new environment. For example, in python:
remote_base = 'http://127.0.0.1:5000'
client = Client(remote_base)
env_id = 'CartPole-v0'
instance_id = client.env_create(env_id)
client.env_step(instance_id, 0)
Testing
This repository contains integration tests, using the python client implementation to send requests to the local server. They can be run using the nose2
framework. From a shell (such as bash) you can run nose2 directly:
cd gym-http-api
nose2
API specification
-
POST
/v1/envs/
- Create an instance of the specified environment
- param:
env_id
-- gym environment ID string, such as 'CartPole-v0' - returns:
instance_id
-- a short identifier (such as '3c657dbc') for the created environment instance. The instance_id is used in future API calls to identify the environment to be manipulated
-
GET
/v1/envs/
- List all environments running on the server
- returns:
envs
-- dict mappinginstance_id
toenv_id
(e.g.{'3c657dbc': 'CartPole-v0'}
) for every env on the server
-
POST
/v1/envs/<instance_id>/reset/
- Reset the state of the environment and return an initial observation.
- param:
instance_id
-- a short identifier (such as '3c657dbc') for the environment instance - returns:
observation
-- the initial observation of the space
-
POST
/v1/envs/<instance_id>/step/
- Step though an environment using an action.
- param:
instance_id
-- a short identifier (such as '3c657dbc') for the environment instance - param:
action
-- an action to take in the environment - returns:
observation
-- agent's observation of the current environment - returns:
reward
-- amount of reward returned after previous action - returns:
done
-- whether the episode has ended - returns:
info
-- a dict containing auxiliary diagnostic information
-
GET
/v1/envs/<instance_id>/action_space/
- Get information (name and dimensions/bounds) of the env's
action_space
- param:
instance_id
-- a short identifier (such as '3c657dbc') for the environment instance - returns:
info
-- a dict containing 'name' (such as 'Discrete'), and additional dimensional info (such as 'n') which varies from space to space
- Get information (name and dimensions/bounds) of the env's
-
GET
/v1/envs/<instance_id>/observation_space/
- Get information (name and dimensions/bounds) of the env's
observation_space
- param:
instance_id
-- a short identifier (such as '3c657dbc') for the environment instance - returns:
info
-- a dict containing 'name' (such as 'Discrete'), and additional dimensional info (such as 'n') which varies from space to space
- Get information (name and dimensions/bounds) of the env's
-
POST
/v1/envs/<instance_id>/monitor/start/
- Start monitoring
- param:
instance_id
-- a short identifier (such as '3c657dbc') for the environment instance - param:
force
(default=False) -- Clear out existing training data from this directory (by deleting every file prefixed with "openaigym.") - param:
resume
(default=False) -- Retain the training data already in this directory, which will be merged with our new data - (NOTE: the
video_callable
parameter from the nativeenv.monitor.start
function is NOT implemented)
-
POST
/v1/envs/<instance_id>/monitor/close/
- Flush all monitor data to disk
- param:
instance_id
-- a short identifier (such as '3c657dbc') for the environment instance
-
POST
/v1/upload/
- Flush all monitor data to disk
- param:
training_dir
-- A directory containing the results of a training run. - param:
api_key
-- Your OpenAI API key - param:
algorithm_id
(default=None) -- An arbitrary string indicating the paricular version of the algorithm (including choices of parameters) you are running.
-
POST
/v1/shutdown/
- Request a server shutdown
- Currently used by the integration tests to repeatedly create and destroy fresh copies of the server running in a separate thread
Contributors
See the [contributors page] (https://github.com/openai/gym-http-api/graphs/contributors)