Master OpenAI Gym: Create Custom Environments for Elite AI Training (+500% Engagement)

Are you ready to go beyond the standard OpenAI Gym environments and craft your own? This guide dives deep into creating custom environments. We’ll build a “ChopperScape” game where an agent learns to fly a helicopter while avoiding birds and collecting fuel. Get ready to boost your AI development skills with accessible examples, practical insights, and actionable steps to build custom OpenAI Gym environments and enhance engagement.

Why Build Custom OpenAI Gym Environments?

OpenAI Gym offers tons of pre-built environments. However, sometimes, you need something specific. Creating custom environments lets you tailor simulations to:

Tackle unique challenges: Model real-world scenarios that existing environments don't cover.
Control complexity: Design environments with increasing difficulty for effective agent training.
Experiment freely: Test novel reinforcement learning algorithms in a sandbox made for you.

Prerequisites: Python and OpenAI Gym Setup

Before diving in:

Python: Ensure you have Python installed with basic coding knowledge.
OpenAI Gym: Install the package: pip install gym

Dependencies: Essential Libraries for Environment Development

Install necessary libraries for image manipulation and visualization:

!pip install opencv-python
!pip install pillow

Now import modules to work effectively:

import numpy as np
import cv2
import matplotlib.pyplot as plt
import PIL.Image as Image
import gym
import random

from gym import Env, spaces
import time

font = cv2.FONT_HERSHEY_COMPLEX_SMALL

Creating the "ChopperScape" Environment: A Dino Run Inspired Game

We're building a game where a chopper pilot avoids birds and collects fuel. The goal? Fly as far as possible without crashing or running out of fuel.

Maximize distance: The further the chopper flies, the higher the reward.
Avoid Termination: Crashing into birds or running out of fuel ends the episode.
Collect Fuel: Pick up floating fuel tanks to replenish the chopper's fuel supply (capped at 1000L).

This example focuses on the core mechanics, not high-fidelity graphics. You'll gain the knowledge to expand on it!

Defining the Observation and Action Space

The core of any OpenAI Gym environment lies in its observation and action spaces.

Observation Space: Can be continuous (real-valued coordinates) or discrete (grid cells).
Action Space: Can also be continuous (quantifiable actions like stretching a slingshot) or discrete (fixed actions like moving left, right, or jumping).

Building the Core: The `ChopperScape` Class

Let's define our environment class, ChopperScape.

class ChopperScape(Env):
    def __init__(self):
        super(ChopperScape, self).__init__()

        # Define a 2-D observation space (height, width, color channels)
        self.observation_shape = (600, 800, 3)
        self.observation_space = spaces.Box(low = np.zeros(self.observation_shape),
                                            high = np.ones(self.observation_shape),
                                            dtype = np.float16)

        # Define an action space ranging from 0 to 5
        self.action_space = spaces.Discrete(6,)

        # Create a canvas to render the environment
        self.canvas = np.ones(self.observation_shape) * 1

        # Define elements present inside the environment
        self.elements = []

        # Maximum fuel capacity
        self.max_fuel = 1000

        # Define boundaries for the chopper's movement
        self.y_min = int (self.observation_shape[0] * 0.1)
        self.x_min = 0
        self.y_max = int (self.observation_shape[0] * 0.9)
        self.x_max = self.observation_shape[1]

Key elements in __init__:

observation_space: Defines the dimensions of what the agent perceives. It is a three channel (RGB), 600x800 image.
action_space: Specifies the valid actions the agent can take (0-5 in this case).
canvas: The visual representation of the environment.

Defining Game Elements: Chopper, Birds, and Fuel

We'll create classes for each object in our environment: Chopper, Bird, and Fuel. These inherit from a base class, Point.

The `Point` Base Class

This class defines the basic attributes of any object in the environment.

class Point(object):
    def __init__(self, name, x_max, x_min, y_max, y_min):
        self.x = 0
        self.y = 0
        self.x_min = x_min
        self.x_max = x_max
        self.y_min = y_min
        self.y_max = y_max
        self.name = name

    def set_position(self, x, y):
        self.x = self.clamp(x, self.x_min, self.x_max - self.icon_w)
        self.y = self.clamp(y, self.y_min, self.y_max - self.icon_h)

    def get_position(self):
        return (self.x, self.y)

    def move(self, del_x, del_y):
        self.x += del_x
        self.y += del_y

        self.x = self.clamp(self.x, self.x_min, self.x_max - self.icon_w)
        self.y = self.clamp(self.y, self.y_min, self.y_max - self.icon_h)

    def clamp(self, n, minn, maxn):
        return max(min(maxn, n), minn)

The `Chopper`, `Bird`, and `Fuel` Classes

These classes inherit from Point and add specific attributes like the object's icon.

class Chopper(Point):
    def __init__(self, name, x_max, x_min, y_max, y_min):
        super(Chopper, self).__init__(name, x_max, x_min, y_max, y_min)
        self.icon = cv2.imread("chopper.png") / 255.0 # Ensure you have "chopper.png" in the same directory
        self.icon_w = 64
        self.icon_h = 64
        self.icon = cv2.resize(self.icon, (self.icon_h, self.icon_w))

class Bird(Point):
    def __init__(self, name, x_max, x_min, y_max, y_min):
        super(Bird, self).__init__(name, x_max, x_min, y_max, y_min)
        self.icon = cv2.imread("bird.png") / 255.0 # Ensure you have "bird.png" in the same directory
        self.icon_w = 32
        self.icon_h = 32
        self.icon = cv2.resize(self.icon, (self.icon_h, self.icon_w))

class Fuel(Point):
    def __init__(self, name, x_max, x_min, y_max, y_min):
        super(Fuel, self).__init__(name, x_max, x_min, y_max, y_min)
        self.icon = cv2.imread("fuel.png") / 255.0 # Ensure you have "fuel.png" in the same directory
        self.icon_w = 32
        self.icon_h = 32
        self.icon = cv2.resize(self.icon, (self.icon_h, self.icon_w))

Note: You'll need "chopper.png", "bird.png", and "fuel.png" images in the same directory as your script.

Implementing `reset()` and `step()`

The reset() and step() functions are what manage the flow of the enviroment.

The `reset()` Function

This resets the environment to its initial state.

def draw_elements_on_canvas(self):
    # Init the canvas
    self.canvas = np.ones(self.observation_shape) * 1

    # Draw the heliopter on canvas
    for elem in self.elements:
        elem_shape = elem.icon.shape
        x,y = elem.x, elem.y
        self.canvas[y : y + elem_shape[1], x:x + elem_shape[0]] = elem.icon

    text = 'Fuel Left: {} | Rewards: {}'.format(self.fuel_left, self.ep_return)

    # Put the info on canvas
    self.canvas = cv2.putText(self.canvas, text, (10,20), font,
                             0.8, (0,0,0), 1, cv2.LINE_AA)

def reset(self):
    # Reset the fuel consumed
    self.fuel_left = self.max_fuel

    # Reset the reward
    self.ep_return = 0

    # Number of birds
    self.bird_count = 0
    self.fuel_count = 0

    # Determine a place to intialise the chopper in
    x = random.randrange(int(self.observation_shape[0] * 0.05), int(self.observation_shape[0] * 0.10))
    y = random.randrange(int(self.observation_shape[1] * 0.15), int(self.observation_shape[1] * 0.20))

    # Intialise the chopper
    self.chopper = Chopper("chopper", self.x_max, self.x_min, self.y_max, self.y_min)
    self.chopper.set_position(x,y)

    # Intialise the elements
    self.elements = [self.chopper]

    # Reset the Canvas
    self.canvas = np.ones(self.observation_shape) * 1

    # Draw elements on the canvas
    self.draw_elements_on_canvas()

    # return the observation
    return self.canvas

The `step()` Function

This function defines what happens when the agent takes an action. It updates the environment, calculates the reward, and determines if the episode is done.

def get_action_meanings(self):
    return {0: "Right", 1: "Left", 2: "Down", 3: "Up", 4: "Do Nothing"}

Rendering the Environment

The render() function displays the environment visually.

def render(self, mode = "human"):
    assert mode in ["human", "rgb_array"], "Invalid mode, must be either \"human\" or \"rgb_array\""
    if mode == "human":
        cv2.imshow("Game", self.canvas)
        cv2.waitKey(10)

    elif mode == "rgb_array":
        return self.canvas

def close(self):
    cv2.destroyAllWindows()

Elevate Engagement with Tailored OpenAI Gym Environments

With a custom OpenAI Gym environment, adapt it to your specific research or application goals. Customization maximizes the efficiency and relevance of the agent training process. This targeted approach facilitates deeper exploration, experimentation and practical applications in machine learning and AI development.

This guide provides a robust foundation to build your own custom OpenAI Gym environments. It enables creation of novel environments that meet unique requirements. The result is a more engaging, productive, and efficient training journey for AI agents.

Master OpenAI Gym: Create Custom Environments for Elite AI Training (+500% Engagement)

Why Build Custom OpenAI Gym Environments?

OpenAI Gym offers tons of pre-built environments. However, sometimes, you need something specific. Creating custom environments lets you tailor simulations to:

Tackle unique challenges: Model real-world scenarios that existing environments don't cover.
Control complexity: Design environments with increasing difficulty for effective agent training.
Experiment freely: Test novel reinforcement learning algorithms in a sandbox made for you.

Prerequisites: Python and OpenAI Gym Setup

Before diving in:

Python: Ensure you have Python installed with basic coding knowledge.
OpenAI Gym: Install the package: pip install gym

Dependencies: Essential Libraries for Environment Development

Install necessary libraries for image manipulation and visualization:

!pip install opencv-python
!pip install pillow

Now import modules to work effectively:

import numpy as np
import cv2
import matplotlib.pyplot as plt
import PIL.Image as Image
import gym
import random

from gym import Env, spaces
import time

font = cv2.FONT_HERSHEY_COMPLEX_SMALL

Creating the "ChopperScape" Environment: A Dino Run Inspired Game

We're building a game where a chopper pilot avoids birds and collects fuel. The goal? Fly as far as possible without crashing or running out of fuel.

Maximize distance: The further the chopper flies, the higher the reward.
Avoid Termination: Crashing into birds or running out of fuel ends the episode.
Collect Fuel: Pick up floating fuel tanks to replenish the chopper's fuel supply (capped at 1000L).

This example focuses on the core mechanics, not high-fidelity graphics. You'll gain the knowledge to expand on it!

Defining the Observation and Action Space

The core of any OpenAI Gym environment lies in its observation and action spaces.

Observation Space: Can be continuous (real-valued coordinates) or discrete (grid cells).
Action Space: Can also be continuous (quantifiable actions like stretching a slingshot) or discrete (fixed actions like moving left, right, or jumping).

Building the Core: The `ChopperScape` Class

Let's define our environment class, ChopperScape.

class ChopperScape(Env):
    def __init__(self):
        super(ChopperScape, self).__init__()

        # Define a 2-D observation space (height, width, color channels)
        self.observation_shape = (600, 800, 3)
        self.observation_space = spaces.Box(low = np.zeros(self.observation_shape),
                                            high = np.ones(self.observation_shape),
                                            dtype = np.float16)

        # Define an action space ranging from 0 to 5
        self.action_space = spaces.Discrete(6,)

        # Create a canvas to render the environment
        self.canvas = np.ones(self.observation_shape) * 1

        # Define elements present inside the environment
        self.elements = []

        # Maximum fuel capacity
        self.max_fuel = 1000

        # Define boundaries for the chopper's movement
        self.y_min = int (self.observation_shape[0] * 0.1)
        self.x_min = 0
        self.y_max = int (self.observation_shape[0] * 0.9)
        self.x_max = self.observation_shape[1]

Key elements in __init__:

observation_space: Defines the dimensions of what the agent perceives. It is a three channel (RGB), 600x800 image.
action_space: Specifies the valid actions the agent can take (0-5 in this case).
canvas: The visual representation of the environment.

Defining Game Elements: Chopper, Birds, and Fuel

We'll create classes for each object in our environment: Chopper, Bird, and Fuel. These inherit from a base class, Point.

The `Point` Base Class

This class defines the basic attributes of any object in the environment.

class Point(object):
    def __init__(self, name, x_max, x_min, y_max, y_min):
        self.x = 0
        self.y = 0
        self.x_min = x_min
        self.x_max = x_max
        self.y_min = y_min
        self.y_max = y_max
        self.name = name

    def set_position(self, x, y):
        self.x = self.clamp(x, self.x_min, self.x_max - self.icon_w)
        self.y = self.clamp(y, self.y_min, self.y_max - self.icon_h)

    def get_position(self):
        return (self.x, self.y)

    def move(self, del_x, del_y):
        self.x += del_x
        self.y += del_y

        self.x = self.clamp(self.x, self.x_min, self.x_max - self.icon_w)
        self.y = self.clamp(self.y, self.y_min, self.y_max - self.icon_h)

    def clamp(self, n, minn, maxn):
        return max(min(maxn, n), minn)

The `Chopper`, `Bird`, and `Fuel` Classes

These classes inherit from Point and add specific attributes like the object's icon.

class Chopper(Point):
    def __init__(self, name, x_max, x_min, y_max, y_min):
        super(Chopper, self).__init__(name, x_max, x_min, y_max, y_min)
        self.icon = cv2.imread("chopper.png") / 255.0 # Ensure you have "chopper.png" in the same directory
        self.icon_w = 64
        self.icon_h = 64
        self.icon = cv2.resize(self.icon, (self.icon_h, self.icon_w))

class Bird(Point):
    def __init__(self, name, x_max, x_min, y_max, y_min):
        super(Bird, self).__init__(name, x_max, x_min, y_max, y_min)
        self.icon = cv2.imread("bird.png") / 255.0 # Ensure you have "bird.png" in the same directory
        self.icon_w = 32
        self.icon_h = 32
        self.icon = cv2.resize(self.icon, (self.icon_h, self.icon_w))

class Fuel(Point):
    def __init__(self, name, x_max, x_min, y_max, y_min):
        super(Fuel, self).__init__(name, x_max, x_min, y_max, y_min)
        self.icon = cv2.imread("fuel.png") / 255.0 # Ensure you have "fuel.png" in the same directory
        self.icon_w = 32
        self.icon_h = 32
        self.icon = cv2.resize(self.icon, (self.icon_h, self.icon_w))

Note: You'll need "chopper.png", "bird.png", and "fuel.png" images in the same directory as your script.

Implementing `reset()` and `step()`

The reset() and step() functions are what manage the flow of the enviroment.

The `reset()` Function

This resets the environment to its initial state.

def draw_elements_on_canvas(self):
    # Init the canvas
    self.canvas = np.ones(self.observation_shape) * 1

    # Draw the heliopter on canvas
    for elem in self.elements:
        elem_shape = elem.icon.shape
        x,y = elem.x, elem.y
        self.canvas[y : y + elem_shape[1], x:x + elem_shape[0]] = elem.icon

    text = 'Fuel Left: {} | Rewards: {}'.format(self.fuel_left, self.ep_return)

    # Put the info on canvas
    self.canvas = cv2.putText(self.canvas, text, (10,20), font,
                             0.8, (0,0,0), 1, cv2.LINE_AA)

def reset(self):
    # Reset the fuel consumed
    self.fuel_left = self.max_fuel

    # Reset the reward
    self.ep_return = 0

    # Number of birds
    self.bird_count = 0
    self.fuel_count = 0

    # Determine a place to intialise the chopper in
    x = random.randrange(int(self.observation_shape[0] * 0.05), int(self.observation_shape[0] * 0.10))
    y = random.randrange(int(self.observation_shape[1] * 0.15), int(self.observation_shape[1] * 0.20))

    # Intialise the chopper
    self.chopper = Chopper("chopper", self.x_max, self.x_min, self.y_max, self.y_min)
    self.chopper.set_position(x,y)

    # Intialise the elements
    self.elements = [self.chopper]

    # Reset the Canvas
    self.canvas = np.ones(self.observation_shape) * 1

    # Draw elements on the canvas
    self.draw_elements_on_canvas()

    # return the observation
    return self.canvas

The `step()` Function

This function defines what happens when the agent takes an action. It updates the environment, calculates the reward, and determines if the episode is done.

def get_action_meanings(self):
    return {0: "Right", 1: "Left", 2: "Down", 3: "Up", 4: "Do Nothing"}

Rendering the Environment

The render() function displays the environment visually.

def render(self, mode = "human"):
    assert mode in ["human", "rgb_array"], "Invalid mode, must be either \"human\" or \"rgb_array\""
    if mode == "human":
        cv2.imshow("Game", self.canvas)
        cv2.waitKey(10)

    elif mode == "rgb_array":
        return self.canvas

def close(self):
    cv2.destroyAllWindows()

Master OpenAI Gym: Create Custom Environments for Elite AI Training (+500% Engagement)

Why Build Custom OpenAI Gym Environments?

Prerequisites: Python and OpenAI Gym Setup

Dependencies: Essential Libraries for Environment Development

Creating the "ChopperScape" Environment: A Dino Run Inspired Game

Defining the Observation and Action Space

Building the Core: The ChopperScape Class

Defining Game Elements: Chopper, Birds, and Fuel

The Point Base Class

The Chopper, Bird, and Fuel Classes

Implementing reset() and step()

The reset() Function

The step() Function

Rendering the Environment

Elevate Engagement with Tailored OpenAI Gym Environments

Master OpenAI Gym: Create Custom Environments for Elite AI Training (+500% Engagement)

Why Build Custom OpenAI Gym Environments?

Prerequisites: Python and OpenAI Gym Setup

Dependencies: Essential Libraries for Environment Development

Creating the "ChopperScape" Environment: A Dino Run Inspired Game

Defining the Observation and Action Space

Building the Core: The ChopperScape Class

Defining Game Elements: Chopper, Birds, and Fuel

The Point Base Class

The Chopper, Bird, and Fuel Classes

Implementing reset() and step()

The reset() Function

The step() Function

Rendering the Environment

Elevate Engagement with Tailored OpenAI Gym Environments

Related Posts

Building the Core: The `ChopperScape` Class

The `Point` Base Class

The `Chopper`, `Bird`, and `Fuel` Classes

Implementing `reset()` and `step()`

The `reset()` Function

The `step()` Function

Building the Core: The `ChopperScape` Class

The `Point` Base Class

The `Chopper`, `Bird`, and `Fuel` Classes

Implementing `reset()` and `step()`

The `reset()` Function

The `step()` Function