gym-lqr 0.0.1

Gym(nasium) Interface for LQR-Family Problems

Gym Interface for LQR-Family Problems

Gym-LQR is a one-stop shop for data-driven LQR research.

Gym-LQR creates a discrete-time LQR environment from a subset of COMPleib problems. Currently supported systems:

• HEx - Helicopter models
• REAx - Reactor models
• DISx - Decentralized interconnected systems
• TG1 - 1072 MVA nuclear-powered turbo-generator
• AGS - Automobile gas turbine
• BDT1 - Realistic model of binary distillation tower
• MFP - Moored floating platform
• UWV - Control surface servo for underwater vehicle
• EBx - Euler-Bernoulli beam
• PAS - Piezoelectric bimorph actuator system design
• TF - Terrain following model
• PSM - Two-area interconnected power system
• NNx - Academic test problems
• DLRx - Models of space structure
• ROCx - Reduced order control problems

Installation

pip install gym-lqr

Requires Python 3.10+.

Basic Usage

Single-agent (Centralized LQR)

Single-agent interface follows a familiar Gymnasium (ex- OpenAI Gym) gymnasium>=0.26 API.

import gymnasium as gym
# Register the LQR environment
import gym_lqr

# Create a Linear HE1 helicopter model from the COMPleib example set
# Discretize at a sampling rate of dt=0.1
env = gym.make('LQR-v0', env_id='HE1', dt=0.1)

# Fix the random seed for reproducibility
env.action_space.seed(1337)

# Reset the environment to generate the first observation
observation, info = env.reset(seed=1337)
for _ in range(100):
    # this is where you would insert your policy
    action = env.action_space.sample()

    # step (transition) through the environment with the action
    # receiving the next observation, reward and if the episode has terminated or truncated
    observation, reward, terminated, truncated, info = env.step(action)

    # If the episode has ended then we can reset to start a new episode
    if terminated or truncated:
        observation, info = env.reset()

env.close()

Multi-agent (Distributed LQR)

In development.

Future Releases

• Support for more COMPleib problems.
• Multi-agent (Distributed LQR) interface based on the PettingZoo API.

References

1. Gymnasium - an API standard for reinforcement learning with a diverse collection of reference environments. Web: https://gymnasium.farama.org/index.html
2. COMPleib: COnstraint Matrix-optimization Problem library. Web: http://www.complib.de/
3. "Predictive Control for Linear and Hybrid Systems" by Francesco Borrelli, Alberto Bemporad, Manfred Morari (2017). Web: http://cse.lab.imtlucca.it/~bemporad/publications/papers/BBMbook.pdf