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How to create and install a custom Open AI Gym Environment to use with Reinforcement Learning

Author: Olav Tollefsen

Introduction

This repository contains two custom OpenAI Gym environments, which can be used by several frameworks and tools to experiment with Reinforcement Learning algorithms. The problem solved in this sample environment is to train the software to control a ventilation system. The goals are to keep an acceptable level of CO2 in the indoor air, while minimizing the energy used for ventilation / heating / cooling.

System Requirements

  • Python 3.6 or higher (64-bit version)
  • PIP
  • Microsoft Visual C++ 2015 Redistributable Update 3 (for Tensorflow)

Installation of the custom Gym environments

Download and install the gym_co2_ventilation directly from GitHub using this command:

$ pip install -e git+https://github.com/olavt/gym_co2_ventilation.git#egg=gym_co2_ventilation

You may need to restart Python in order for the new Gym environmnet to be available for use.

Using the custom Gym environment (simulator)

To use the new custom Gym environmnet, you need to import it into your code like this:

import gym
# This will trigger the code to register the custom environment with Gym
import gym_co2_ventilation 

env = gym.make('CO2VentilationSimulator-v0')
env.reset()
for _ in range(360):
    env.render()
    action = env.action_space.sample()  # take a random action
    env.step(action)

You should see output like this:

CO2VentilationSimulatorEnv - Version 0.0.1
Fan speed=1, CO2=776
Fan speed=1, CO2=791.0
Fan speed=4, CO2=806.0
Fan speed=2, CO2=776.0
Fan speed=1, CO2=786.0
Fan speed=4, CO2=801.0
Fan speed=4, CO2=771.0
Fan speed=4, CO2=741.0

How does the custom environment work (simulator)?

The main logic of the custom environment can be found in this file: gym_co2_ventilation/gym_co2_ventilation/envs/co2_ventilation_simulator_env.py

Reinforcement Learining using the custom gym environment (simulator)

An example on how to use the custom gym environment for Reinforcement Learning can be found here: gym_co2_ventilation/examples/test_keras_rl.py

Training in production

In many cases it`s very difficult to get approperiate historical data to be able to pre-train the models. In such cases one may need to start the training while in production. It is very important that the scenario allows for mistakes without too large negative consequence. If an algorithm for CO2-based control of a ventilation system does mistakes it can either cause bad air quality (fan speed too low) or higher energy consumption (fan speed to high).

How does the custom environment work (production)?

The main logic of the custom environment for a train in production scenario can be found in this file: gym_co2_ventilation/gym_co2_ventilation/envs/co2_ventilation_production_env.py

Reinforcement Learining using the custom gym environment (production)

An example on how to use the custom gym environment for Reinforcement Learning in production can be found here: gym_co2_ventilation/examples/test_keras_rl_production.py