My python 3.* fork

Many people are running into the "cant jsonify float" bug. This fork solves that, at least in my implementation which is javas DL4J. The old repo shouldnt be changed as it will break to many things. There is a new gym for 3.0 by openai. For Those of you looking to use this fork Ill exaplin how I used it with DL4J. Sorry for so much info but better to much than not enough.

1.Get a python install working with tensorflow To do this you should install pycharm. I assume you already have your normal ML installation working. Now you need to get the tensorflow version working. A good resource currently is https://github.com/jeffheaton/t81_558_deep_learning and check out his youtube channel as well for very current python istalls with 3.* and tensorflow 2.*

2. Get a gym and atari install working with tensorflow in pycharm If you followed the instruction above you should have a tensorflow install with tensorflow as your execution enviroment in your ide. Within your ide install the gym api. This might be tricky. When you can run the script included in this repo, test.py, you should have the basic gyms plus the atari

3.Set up the server Within your pycharm ide and the tensorflow enviroment perform the normal installation instructions below. Then run your client software to see if you get a connection.

A few words of caution. Your pycharm terminal is not normally the same as your execution enviroment if you follow my instructions above. Keep that in mind when executing and doing installs. Secondly, if your not a python person like me, you may not realize that python allows for multiple virtual enviroments using the anaconda tool. I made two virtual enviroments per the jeff heaton method. The first is python 3.7 with TF 2 and the second is python 3.6 with TF 1.15. This is so I can follow or debug different learning examples. I highly recomend it!

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 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/<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 /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

• 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 native env.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)