Custom Environments in AI & Artificial Intelligence

To build a custom world, you start by inheriting from gym.Env. This base class provides the structure that RL libraries expect. In the __init__ method, you define the 'static' parts: the Action Space (what can the agent do?) and the Observation Space (what can the agent see?). This is like defining the hardware of a robot or the rules of a game before the first match begins.

The step() method is where the 'physics' of your world happens. It takes an Action as input and updates the internal state of the environment. You must calculate the Reward—the most critical part of the engineering. If you reward the wrong things, the agent will 'hack' your world. The function returns the five-part feedback tuple (observation, reward, terminated, truncated, info) that drives the learning loop.

Once built, you can Register your environment with a unique ID, allowing you to create instances of it using gym.make('MyEnv-v0'). Before training, it is vital to Test the environment using a 'Random Agent' and the built-in check_env utility. This ensures that your spaces match your data and that the environment doesn't crash or produce invalid rewards during long training runs.

Atari and CartPole are great for learning, but to solve real problems, you need to build your own worlds. Creating a custom Gymnasium environment is the key to applying RL to business, science, and robotics.

To build an environment, you inherit from gym.Env and implement four critical methods: __init__, reset, step, and render.

In the step function, you define your 'Physics'. You calculate how the state changes based on the action and return the corresponding reward.

Checkpoint: Where do you define the 'rules' and 'logic' of your custom world?

• →In the __init__ method
• →In the step() method

Spaces are vital. You must define an action_space and an observation_space. This tells your RL agent exactly what it's dealing with before it starts its first episode.

Once your environment is registered, you can use any RL library—like Stable Baselines3 or Ray RLLib—to solve it with a single line of code.

Checkpoint: What is the purpose of the 'observation_space' in a custom environment?

• →To set the background color
• →To define the shape and range of the data the agent will 'see'

Custom environments mastered! You've learned to build your own worlds. Ready to move beyond values and learn about Policy Gradients?

Implement a Real Environment Step. Finish the step function that moves the agent's state forward or backward based on its action.