A Tour of Reinforcement Learning: The View from Continuous Control (1806.09460v2)

Abstract: This manuscript surveys reinforcement learning from the perspective of optimization and control with a focus on continuous control applications. It surveys the general formulation, terminology, and typical experimental implementations of reinforcement learning and reviews competing solution paradigms. In order to compare the relative merits of various techniques, this survey presents a case study of the Linear Quadratic Regulator (LQR) with unknown dynamics, perhaps the simplest and best-studied problem in optimal control. The manuscript describes how merging techniques from learning theory and control can provide non-asymptotic characterizations of LQR performance and shows that these characterizations tend to match experimental behavior. In turn, when revisiting more complex applications, many of the observed phenomena in LQR persist. In particular, theory and experiment demonstrate the role and importance of models and the cost of generality in reinforcement learning algorithms. This survey concludes with a discussion of some of the challenges in designing learning systems that safely and reliably interact with complex and uncertain environments and how tools from reinforcement learning and control might be combined to approach these challenges.

• The paper presents a systematic framework for applying reinforcement learning to continuous control, using the LQR problem to benchmark performance differences.
• It contrasts model-based methods that estimate system dynamics with model-free techniques such as policy gradients for optimal control.
• The findings underscore the benefit of integrating robust control methodologies with machine learning to enhance safety and sample efficiency in real-world applications.

A Survey on Reinforcement Learning with Continuous Control

The paper "A Tour of Reinforcement Learning: The View from Continuous Control" by Benjamin Recht offers a comprehensive examination of reinforcement learning (RL) from the vantage point of optimization and control, particularly emphasizing continuous control applications. This document presents an overview of RL's formulation, terminology, and common experimental implementations, with a focus on evaluating solution paradigms via a case paper of the Linear Quadratic Regulator (LQR) problem under unknown dynamics conditions.

Overview of Reinforcement Learning

Reinforcement learning is described as the paper of leveraging historical data to inform future decisions in manipulating dynamical systems. Although this aligns closely with control theory, the two fields have evolved distinct approaches to similar challenges. As RL continues to proliferate into complex interactive technologies, the necessity for methodologies that are both safe and reliable becomes paramount. Consequently, integrating control tools into RL could bolster robustness and safety.

Methodological Analysis

The paper categorizes RL solutions into two broad methodologies: model-free and model-based. Model-free strategies, which often employ intricate model-free predictions from data, include Policy Search and Approximate Dynamic Programming (ADP). Model-based approaches construct a model based on collected data to estimate and navigate the system dynamics.

Model-Based Approaches

Model-based methodologies estimate a model that approximates the system dynamics. Once a model is estimated via supervised learning, it can be utilized within a nominal control framework. This is demonstrated in the Linear Quadratic Regulator (LQR) setting, where understanding and stabilizing the system is crucial for efficacy.

Model-Free Approaches

Model-free strategies, particularly through ADP, aim to approximate the optimal control cost by leveraging BeLLMan’s principle of optimality. These methods frequently face challenges related to sampling inefficiency compared to model-based approaches. Moreover, direct policy search methods, including policy gradient algorithms, often exhibit excessive variance, making them less practical without significant computational resources.

Evaluating the Linear Quadratic Regulator

A pivotal aspect of the paper is its examination of the LQR problem as a baseline for understanding reinforcement learning's efficiency. By focusing on LQR, the paper elucidates various RL methods' trade-offs between sample complexity and control accuracy. The findings suggest that model-based approaches leveraging robust control techniques, such as Coarse-ID Control, can achieve non-asymptotic bounds on performance, substantially outperforming model-free methods in stability and sample efficiency.

Practical and Theoretical Implications

The manuscript underscores the interaction between theoretical insight and practical application, advocating for integrating machine learning with robust control to ensure safe interaction with uncertain physical environments. Emphasizing the balance between learning from models and optimizing value functions could lead to robust and efficient system control in real-time.

Future Directions

The paper lays the groundwork for future advancements, proposing the exploration of:

• Merging perception and control to achieve end-to-end control from sensor data.
• Investigating adaptive control beyond episodic reinforcement learning, where real-time learning demands different strategies.
• Implementing strategies that involve humans in the loop, complicating models with human-robot interaction dynamics.

Conclusion

"A Tour of Reinforcement Learning: The View from Continuous Control" provides a structured survey that bridges RL and control theory, emphasizing the importance of models in achieving robust and efficient solutions. It calls for collaboration between these fields to develop strategies that can ensure machine learning systems provide actionable intelligence, balancing safety and innovation.