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Letter World: The Ground Environment

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Overview

The ground environment in our framework refers to the base environment that defines the world dynamics, independent of any reward structure. For Letter World, this is a grid-based environment implemented as a standard OpenAI Gymnasium environment. See our paper for more details.

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Environment Description

Letter World consists of:
  • A 3×7 grid where the agent can move in four directions
  • Special positions labeled with letters ‘A’, ‘B’, and ‘C’
  • Stochastic behavior: when visiting position ‘A’, it may randomly change to ‘B’ (with 50% probability)
  • A simple observation space containing the agent’s position and a flag indicating if A has changed to B.
Here’s a visualisation of the environment: 
+-------------+
|. . . . . . .|
|A . x . . C .|
|. . . . . . .|
+-------------+
Where:
  • x represents the agent (starting at position [1,3])
  • A represents the position of letter A (which may change to B)
  • C represents the position of letter C
  • . represents empty spaces

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Implementation Details

The Letter World environment is implemented as a subclass of gymnasium.Env, following the standard Gym interface. 
import gymnasium as gym
import numpy as np

class LetterWorld(gym.Env):
    """The Letter World environment."""

    RIGHT = 0
    LEFT = 1
    UP = 2
    DOWN = 3
    A_POSITION = np.array([1, 1])
    C_POSITION = np.array([1, 5])
    N_ROWS = 3
    N_COLS = 7

    def __init__(self) -> None:
        """Initialize the environment."""
        self.action_space = gym.spaces.Discrete(4)  # Four movement directions
        self.observation_space = gym.spaces.Box(
            low=0, high=100, shape=(3,), dtype=np.int32
        )  # [symbol_seen, row, col]

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Action Space

The environment supports four actions for movement:
  • 0: Move right
  • 1: Move left
  • 2: Move up
  • 3: Move down
These actions are defined as constants in the LetterWorld class for better code readability.

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Observation Space

The observation is a 3-dimensional vector:
  • First element: Binary flag (0/1) indicating if A has changed to B
  • Second element: Row position of the agent
  • Third element: Column position of the agent
This is implemented as a Box space with shape (3,) in the code.

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Movement Mechanics

When the agent attempts to move outside the grid boundaries, it remains in the same position. Here’s the code that handles movement: 
def _update_agent_position(self, action: int) -> None:
    """Moves that take agent out of the grid leave it in the same position."""
    row, col = self.agent_position

    if action == self.RIGHT:
        n_row = row
        n_col = col + 1 if col < self.N_COLS - 1 else col
    elif action == self.LEFT:
        n_row = row
        n_col = col - 1 if col > 0 else col
    elif action == self.UP:
        n_col = col
        n_row = row - 1 if row > 0 else row
    elif action == self.DOWN:
        n_col = col
        n_row = row + 1 if row < self.N_ROWS - 1 else row
    else:
        raise ValueError(f"Invalid action {action}.")
    self.agent_position = (n_row, n_col)
The method includes boundary checks for each direction to prevent the agent from moving outside the grid.

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Stochastic Behavior

The environment includes a stochastic element: when the agent visits position A, there’s a 50% chance that the symbol changes from ‘A’ to ‘B’. 
def step(self, action: int) -> tuple[np.ndarray, float, bool, bool, dict]:
    """Take a step in the environment."""
    self._update_agent_position(action)

    if not self.symbol_seen and np.array_equal(
        self.agent_position, self.A_POSITION
    ):
        self.symbol_seen = np.random.random() < 0.5
    return self._get_obs(), 0, False, False, {}
This stochastic behavior creates an interesting challenge for reinforcement learning algorithms, as they must adapt to uncertain outcomes. The change in symbol is tracked by the symbol_seen flag in the observation.

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Using the Environment

The environment follows the standard Gym interface, making it easy to use with existing RL algorithms: 
# Create the environment
env = LetterWorld()

# Reset to get initial observation
observation, info = env.reset()

# Take random actions
for _ in range(10):
    action = env.action_space.sample()  # Random action
    observation, reward, terminated, truncated, info = env.step(action)
    
    # Visualize the state
    env.render()
    
    if terminated or truncated:
        observation, info = env.reset()

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Environment Visualization

When you call render(), the environment prints a colored grid representation to the console. Here’s what it looks like at different states:

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Initial State

+---------------+
|. . . . . . . .|
|. A . x . . C .|
|. . . . . . . .|
+---------------+

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After Moving to Position A

+---------------+
|. . . . . . . .|
|. x . . . . C .|
|. . . . . . . .|
+---------------+

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After Symbol Change (A to B)

+---------------+
|. . . . . . . .|
|. B . . . . C .|
|. . . x . . . .|
+---------------+
Note that the environment itself doesn’t provide rewards - the reward structure will be defined by the RM/CRM we’ll connect to this environment.

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Key Points

  • The base environment is reward-free - it only defines the dynamics of the world
  • Navigation is deterministic, but the state transition at position A is stochastic
  • The environment follows the standard Gym interface for compatibility with RL algorithms
  • The agent’s task will be defined by an RM/CRM, which will assign rewards based on the sequence of visited letters

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Next Steps