API Reference

This section provides detailed documentation for all InfraLib modules and classes.

Core Modules

• Models Module
  • Base Models
  • Dynamics Models
  • Cost Models
  • Budget Models
  • Hierarchy Models
  • Metadata Models
• Environments Module
  • Base Environment
  • Simple Environment
• Simulator Module
  • Main Simulator
• Visualization Module
  • Dashboard
  • Network Visualization
  • Plots
  • Simulator Plots

infralib

InfraLib: Infrastructure Management Library for RL and Optimization.

InfraLib is a comprehensive Python library for modeling, simulating, and analyzing large-scale infrastructure management problems. It provides a realistic and granular representation of infrastructure systems by integrating hierarchical models that capture intricate relationships between components and facilities.

Key Features:

• Stochastic deterioration modeling with partial observability

• Gymnasium-compatible RL environments

• Scalable simulation supporting millions of components

• Rich visualization and analysis tools

• Flexible model architecture with dependency injection

• Web-based human interface for expert data collection

Example

Basic usage with default models:

from infralib.models.dynamics import WeibullDynamics
from infralib.models.cost import SimpleCost
from infralib.models.budget import FixedBudget
from infralib.simulator import Simulator
import numpy as np

# Setup models
dynamics = WeibullDynamics(n_states=10)
cost = SimpleCost()
budget = FixedBudget(initial_budget=10000)

# Create and run simulator
sim = Simulator(dynamics, cost, budget)
sim.reset(n_components=5)
actions = np.array([0, 1, 2, 0, 1])  # do_nothing, inspect, repair, do_nothing, inspect
states, info = sim.step(actions)

print(f"Total cost: {info['total_cost']:.2f}")
print(f"Failures: {info['failures']}")

Creating a custom RL environment:

from infralib.envs.simple import SimpleInfraEnv
import stable_baselines3 as sb3

# Create environment
env = SimpleInfraEnv(n_components=5, max_budget=10000)

# Train RL agent
model = sb3.PPO('MlpPolicy', env, verbose=1)
model.learn(total_timesteps=100000)

# Evaluate policy
obs, info = env.reset()
for _ in range(100):
    action, _states = model.predict(obs)
    obs, reward, done, truncated, info = env.step(action)
    if done or truncated:
        obs, info = env.reset()

Modules:

models: Core models for dynamics, cost, budget, hierarchy, and metadata envs: Gymnasium-compatible RL environments simulator: High-performance infrastructure simulation engine visualize: Visualization and analysis tools infraio: Data input/output utilities

Note

InfraLib requires Python 3.12+ and depends on NumPy, SciPy, Gymnasium, and other scientific computing libraries. See the documentation for complete installation instructions.