Creating Virtual Worlds: AI Agent Environments

Photo by Stefan Cosma on Unsplash

Hey folks! 👋 Today we’re diving into something super cool — creating environments for our AI agents to live in. Think of it as building a sandbox where our agents can play, learn, and do their thing.

This is the 4th article in the 30-day AI Agents series. Check out the first 3 articles if you haven’t already.

Previous Articles: ( Day 1, Day 2, Day 3)

What We’re Building

We’re going to create:

• A customizable grid world environment
• Different types of environments (static and dynamic)
• Reward systems that make sense
• Ways to track what our agents are doing

Let’s jump in!

Building Our First Environment

from dataclasses import dataclass
from typing import List, Tuple, Dict, Optional
import numpy as np
from enum import Enum
import random

class CellType(Enum):
    EMPTY = 0
    WALL = 1
    GOAL = 2
    TRAP = 3
    AGENT = 4

@dataclass
class Position:
    x: int
    y: int
    
    def __add__(self, other):
        return Position(self.x + other.x, self.y + other.y)
    
    def __eq__(self, other):
        return self.x == other.x and self.y == other.y

class GridWorld:
    def…