Very basic REST API for OpenAI gym

gymserver.py

#!flask/bin/python
import gym
from flask import Flask, Response, jsonify
import datetime

app = Flask(__name__)

envname = 'CartPole-v0'
env = gym.make(envname)
env.reset()

@app.route('/')
def index():
    return "Hello, World!"

@app.route('/monitor/<string:state>', methods=['GET'])
def monitor(state):
    if state == 'on':
        outdir = '/tmp/' + envname + '-results-' + str(datetime.datetime.now())
        global env
        env = gym.make(envname) # an environment needs to be re-created so that the monitor can be started again
        env.monitor.start(outdir, force=True)
        env.reset()
    else:
        env.monitor.close()
    return jsonify({'monitor': state})

@app.route('/act/<int:action>', methods=['GET'])
def act(action):
    ob, reward, done, _ = env.step(action)
    if done:
        env.reset() # automatically reset when done
    return jsonify({'observation': ob.tolist(), 'reward': reward, 'done': done})

@app.route('/reset', methods=['GET'])
def reset():
    env.reset()
    return jsonify({'reset': True})

if __name__ == '__main__':
    app.run(debug=True)

Hi !
Is this worked?

Do you know how to deploy a trained Deep Reinforcement model to Flask API and render in browser?