End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks (1903.08792v1)

Abstract: Reinforcement Learning (RL) algorithms have found limited success beyond simulated applications, and one main reason is the absence of safety guarantees during the learning process. Real world systems would realistically fail or break before an optimal controller can be learned. To address this issue, we propose a controller architecture that combines (1) a model-free RL-based controller with (2) model-based controllers utilizing control barrier functions (CBFs) and (3) on-line learning of the unknown system dynamics, in order to ensure safety during learning. Our general framework leverages the success of RL algorithms to learn high-performance controllers, while the CBF-based controllers both guarantee safety and guide the learning process by constraining the set of explorable polices. We utilize Gaussian Processes (GPs) to model the system dynamics and its uncertainties. Our novel controller synthesis algorithm, RL-CBF, guarantees safety with high probability during the learning process, regardless of the RL algorithm used, and demonstrates greater policy exploration efficiency. We test our algorithm on (1) control of an inverted pendulum and (2) autonomous car-following with wireless vehicle-to-vehicle communication, and show that our algorithm attains much greater sample efficiency in learning than other state-of-the-art algorithms and maintains safety during the entire learning process.

End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks

This paper addresses a critical limitation in the deployment of Reinforcement Learning (RL) algorithms for real-world applications: the absence of safety guarantees. Traditional RL often explores unsafe actions leading to potential system failures, especially in safety-critical environments. The proposed solution introduces a novel framework combining model-free RL with model-based Control Barrier Functions (CBFs), alongside an online learning approach to system dynamics. This integration aims to provide end-to-end safety during the learning process while enhancing efficiency in policy exploration.

Framework Overview

The proposed RL-CBF framework involves three key components:

1. Model-Free RL Controller: This aspect leverages the power of RL algorithms to learn high-performance control policies.
2. Model-Based CBF Controller: This ensures safety by restricting the policy space to safe actions only, effectively preventing unsafe exploration.
3. Gaussian Processes (GPs): GPs are utilized to model and learn the system dynamics online, providing probabilistic safety guarantees by capturing uncertainties in the system.

Key Contributions

The paper introduces a controller synthesis algorithm, RL-CBF, that successfully integrates RL with CBF techniques to provide safety guarantees during the learning process. Notably, this integration does not depend on the specific RL algorithm in use, making it a versatile approach that can be combined with any existing model-free RL method. RL-CBF guides the exploration process by constraining exploration to safe regions, which in turn enhances sample efficiency.

Experimental Validation

The efficacy of the RL-CBF algorithm was demonstrated through simulations on two nonlinear control tasks:

• Inverted Pendulum: The algorithm maintained safety throughout and exhibited superior learning efficiency compared to standard RL algorithms (TRPO and DDPG).
• Autonomous Car Following: The simulations demonstrated no occurrences of safety violations, and the RL-CBF variants outperformed traditional RL in terms of learning rate and reward outcomes.

Implications and Future Directions

This research holds promising implications for deploying RL in real-world, safety-critical systems such as autonomous vehicles and robotic control. By ensuring that the learning process itself remains within safe operational limits, RL-CBF bridges a crucial gap allowing RL to transition from simulations to real hardware implementations.

Future developments could explore more sophisticated model learning techniques and dynamic safe set adjustments. Additionally, while the framework currently assumes a predefined safe set facilitated by a valid CBF, extensions to automatically learn or adapt safe sets could enhance applicability in more complex environments.

Overall, the RL-CBF framework serves as a powerful approach for achieving safe and efficient learning in complex and uncertain control tasks, opening new avenues for practical, real-world applications of RL technologies.