boltcrypt 0.1.1

Boltcrypt environment

BoltCrypt: Procedural Dungeon RL Environment

BoltCrypt is a lightweight, OpenAI Gymnasium-compatible environment featuring procedurally generated dungeons. It challenges Reinforcement Learning agents (and humans!) to navigate complex layouts, solve sokoban-style boulder puzzles, and manage inventory items like keys to reach the exit.

🏰 Features

Procedural Generation: Every reset generates a unique dungeon layout based on configurable parameters (density, connectivity, room size). Puzzle Mechanics: Includes boulder-pushing puzzles and locked doors that require finding a key. Gymnasium API: Fully compatible with standard RL workflows. Pygame Visualization: A built-in harness to play manually or watch your agent learn in real-time. Flexible Observation Space: Provides local room grids, global coordinates, and inventory status. 🛠 Installation Since this project uses condavenv, ensure you have your environment active:

# Example if using conda directly
conda activate <your-env-name>
pip install gymnasium pygame numpy matplotlib boltcrypt

🚀 Getting Started

Play Manually

Test the dungeon generation and mechanics yourself using the Pygame harness:

python -m boltcrypt.game.boltcrypt_game

Arrows: Move the agent. R: Reset/Regenerate the dungeon. Goal: Find the key (if required) and reach the green Exit tile.

Alternatively, play in a terminal-based CLI interface:

python -m boltcrypt.game.boltcrypt_cli

WASD/Arrow Keys: Move the agent. R: Reset/Regenerate the dungeon.

Train an Agent

The project includes a tabular Q-Learning implementation to demonstrate how an agent can "memorize" a specific dungeon layout:

python -m boltcrypt.examples.tabular_q

Benchmark Performance

Test environment speed with the included benchmark:

python -m boltcrypt.examples.speed_test

⚙️ Configuration

The DungeonGenerator and BoltCrypt environment can be customized via a config dictionary:

Parameter Description

min_dist: Minimum Manhattan distance between Start and Exit. mean_rooms: Average number of rooms in the dungeon. connectivity: Probability of creating loops between rooms (0.0 = Tree, 1.0 = Highly connected). puzzle_density: Chance of a room containing a boulder puzzle.
key_puzzle_prob: Chance that the exit is locked and a key is hidden in a leaf room.
randomize_end_distance: If True, randomizes the actual distance from min_dist to 2*min_dist.

🗺 Tile Legend

⬜ Floor: Walkable space.
⬛ Wall: Impassable.
🚪 Door: Transitions between rooms (may be locked by puzzles).
🟩 Exit: Your goal!
🔴 Switch: Target for boulders.
🟤 Boulder: Can be pushed onto switches.
🟡 Key: Required to open locked exit rooms.

🤖 Observation Space

The environment returns a dictionary:
grid: A 10x10 local view of the current room.
agent_pos: (x, y) coordinates within the room.
global_pos: (gx, gy) coordinates in the dungeon layout.
inventory: Binary flag (1 if holding a key)

📦 Wrappers & Extensions

BoltCrypt includes several gym.Wrapper implementations to modify observations or rewards, making it a versatile testbed for different RL paradigms.

📝 Natural Language Wrapper (NaturalLanguage)

The crown jewel for testing Reasoning LLMs. This wrapper transforms the numeric observation space into a rich, descriptive narrative. Instead of a grid, the agent receives a text-based description of its surroundings.
Dynamic Narrative: Provides room dimensions, relative positions of doors, boulder locations, and puzzle statuses (e.g., "A loud mechanical clank echoes! The doors unlock.").
LLM Ready: Accepts string inputs like "NORTH", "SOUTH", "EAST", or "WEST" in the step() function.
Physics Logic: Includes an "Adventurer's Manual" to explain game rules to an LLM via the observation stream.

🌫️ Fog of War (FogOfWar)

Transforms the global room view into a partially observable environment.
Vision Range: Limits the grid observation to a (2v+1) \times (2v+1) window centered on the agent.
Memory Challenge: Forces agents to map the room internally rather than having perfect spatial information.

🏆 Room Discovery Reward (RoomDiscoveryReward)

Combats sparse rewards in large dungeons by incentivizing exploration.
Exploration Bonus: Grants a small configurable reward (e.g., +0.1) the first time the agent enters a new room in the dungeon.
Global Navigation: Helps agents learn the layout of the "macro-dungeon" before they’ve found the final exit.

🛠 Usage Example
You can stack wrappers to create complex experimental setups:

import gymnasium as gym
from boltcrypt.env import BoltCrypt
from boltcrypt.wrapper import NaturalLanguage, RoomDiscoveryReward

env = BoltCrypt()
env = RoomDiscoveryReward(env, discovery_reward=0.5)
env = NaturalLanguage(env)

# Now the agent receives text and extra rewards for exploration!
obs, info = env.reset()
print(obs) 

action = "NORTH"
obs, reward, done, trunc, info = env.step(action)

Happy Dungeon Crawling! 🗝️🏹