Controlgym: A Comprehensive Benchmark for Reinforcement Learning in Large-Scale Control Systems

Temel Kavramlar

Controlgym is a library of 36 industrial control settings and 10 infinite-dimensional partial differential equation (PDE)-based control problems, designed to serve as a comprehensive benchmark for evaluating the performance and scalability of reinforcement learning (RL) algorithms in large-scale control systems.

Özet

The paper introduces controlgym, a Python library that provides a diverse set of control environments for benchmarking reinforcement learning (RL) algorithms. The environments span a wide range of applications, including industrial control settings from sectors like aerospace, cyber-physical systems, ground and underwater vehicles, and power systems, as well as large-scale control problems governed by partial differential equations (PDEs) in fluid dynamics and physics.
The key highlights of controlgym are:

Linear control environments: The library includes 36 linear control environments from various industries, which can be used to validate theoretical developments in RL for linear optimal control, robust control, dynamic games, estimation, and filtering.

PDE control environments: The library provides 10 PDE-based control environments, where the state dimensionality can be extended to infinity while preserving the intrinsic dynamics. This feature is crucial for assessing the scalability of RL algorithms.

Customizable dynamics: For the linear PDE environments, the authors provide explicit state-space models, allowing users to tune the open-loop system dynamics by adjusting the physical parameters of the PDEs.

Gym-compliant: All environments in controlgym are integrated within the OpenAI Gym/Gymnasium (Gym) framework, enabling the direct application of standard RL algorithms like stable-baselines3.

The authors demonstrate the usage of controlgym by providing examples of applying model-based controllers and model-free RL algorithms, such as the linear-quadratic-Gaussian (LQG) controller and the proximal policy optimization (PPO) algorithm, to the control environments.

İstatistikler

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Önemli Bilgiler Şuradan Elde Edildi

Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning Algorithms

by Xian... : arxiv.org 04-25-2024

https://arxiv.org/pdf/2311.18736.pdf

Daha Derin Sorular

How can the controlgym environments be extended to incorporate more realistic features, such as nonlinear dynamics, state and input constraints, or partial observability, to better reflect real-world control applications

Incorporating more realistic features into the controlgym environments can significantly enhance their applicability to real-world control applications. One approach to introducing nonlinear dynamics is by expanding the set of PDE control environments to include equations like the Navier-Stokes equations for fluid dynamics or the reaction-diffusion equations for chemical processes. These nonlinear dynamics can capture more complex and realistic behaviors observed in many control systems.
To address state and input constraints, the controlgym environments can be modified to include bounds on the state variables and control inputs. By incorporating constraints, the control algorithms can learn to operate within the limits imposed by the system, leading to more robust and practical control policies. Additionally, partial observability can be introduced by limiting the information available to the agent, simulating scenarios where only a subset of the system's state is observable. This can challenge the algorithms to make decisions based on incomplete information, mimicking real-world control scenarios where full state information may not be available.
By integrating these features, controlgym can provide a more comprehensive and realistic testing ground for reinforcement learning algorithms, enabling researchers to develop and evaluate controllers that are better suited for practical control applications.

What are the potential limitations or drawbacks of the PDE control environments in controlgym, and how could they be addressed to make the benchmark more comprehensive

While the PDE control environments in controlgym offer a rich set of scenarios for benchmarking reinforcement learning algorithms, there are potential limitations and drawbacks that need to be addressed to enhance the benchmark's comprehensiveness.
One limitation is the computational complexity associated with solving PDEs, especially for high-dimensional systems or nonlinear equations. This can lead to longer training times and increased computational resources required for running experiments. To mitigate this limitation, techniques like model reduction or approximation methods can be employed to simplify the PDEs without compromising the essential dynamics, making them more tractable for reinforcement learning algorithms.
Another drawback is the lack of uncertainty modeling in the PDE control environments. Real-world systems are often subject to uncertainties and disturbances that can impact the control performance. By incorporating stochastic elements or noise models into the PDEs, controlgym can better simulate the uncertainties present in practical control applications, allowing researchers to develop more robust and adaptive control strategies.
Furthermore, the current set of PDE control environments may not cover the full spectrum of control challenges faced in diverse industries. To address this limitation, expanding the range of PDEs to include more diverse and complex systems, such as multi-agent systems or interconnected networks, can provide a more comprehensive benchmark for evaluating the scalability and effectiveness of reinforcement learning algorithms in control settings.

Beyond reinforcement learning, how could the controlgym environments be utilized to advance other areas of control theory, such as model predictive control, adaptive control, or robust control

Beyond reinforcement learning, the controlgym environments can serve as valuable testbeds for advancing various areas of control theory, including model predictive control, adaptive control, and robust control.
In the context of model predictive control (MPC), the controlgym environments can be utilized to develop and evaluate MPC algorithms that optimize control actions over a finite time horizon. By formulating the control problem within the controlgym environments, researchers can assess the performance of MPC in handling constraints, uncertainties, and nonlinear dynamics, leading to more effective and reliable control strategies.
For adaptive control, the controlgym environments can be used to test and validate adaptive control algorithms that adjust the controller parameters based on system identification and online learning. By exposing the adaptive controllers to varying environments and system dynamics, researchers can study the adaptability and robustness of these algorithms in real-time control scenarios.
In the realm of robust control, controlgym can be leveraged to investigate the robustness of control algorithms to disturbances, uncertainties, and variations in system parameters. By introducing perturbations or uncertainties into the controlgym environments, researchers can evaluate the ability of robust control strategies to maintain stability and performance under adverse conditions, enhancing the resilience of control systems in practical applications.
Overall, the controlgym environments offer a versatile platform for exploring and advancing different areas of control theory, providing researchers with a comprehensive toolkit for developing and testing a wide range of control algorithms and strategies.