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https://github.com/openai/gym-http-api

API to access OpenAI Gym from other languages via HTTP
https://github.com/openai/gym-http-api

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API to access OpenAI Gym from other languages via HTTP

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README 

        
**Status:** Archive (code is provided as-is, no updates expected)



gym-http-api

============



This project provides a local REST API to the [gym](https://github.com/openai/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 python. 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 server from the command line, run this:



    python gym_http_server.py



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 mapping `instance_id` to `env_id`

	    (e.g. `{'3c657dbc': 'CartPole-v0'}`) for every env on the server



  * POST `/v1/envs//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//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//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 `/v1/envs//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



  * POST `/v1/envs//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 native

    `env.monitor.start` function is NOT implemented)



  * POST `/v1/envs//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)