Responsive Safety in Reinforcement Learning by PID Lagrangian Methods (2007.03964v1)

Abstract: Lagrangian methods are widely used algorithms for constrained optimization problems, but their learning dynamics exhibit oscillations and overshoot which, when applied to safe reinforcement learning, leads to constraint-violating behavior during agent training. We address this shortcoming by proposing a novel Lagrange multiplier update method that utilizes derivatives of the constraint function. We take a controls perspective, wherein the traditional Lagrange multiplier update behaves as \emph{integral} control; our terms introduce \emph{proportional} and \emph{derivative} control, achieving favorable learning dynamics through damping and predictive measures. We apply our PID Lagrangian methods in deep RL, setting a new state of the art in Safety Gym, a safe RL benchmark. Lastly, we introduce a new method to ease controller tuning by providing invariance to the relative numerical scales of reward and cost. Our extensive experiments demonstrate improved performance and hyperparameter robustness, while our algorithms remain nearly as simple to derive and implement as the traditional Lagrangian approach.

• The paper introduces a PID Lagrangian method for RL safety by integrating proportional and derivative control with traditional Lagrange multiplier updates.
• The enhanced update mechanism reduces constraint oscillations and overshoots, achieving robust safety compliance in simulated tasks such as PointGoal and DoggoButton.
• The study demonstrates improved control efficiency and reward-scale invariance, paving the way for scalable, safe RL in critical applications.

Responsive Safety in Reinforcement Learning by PID Lagrangian Methods

The paper "Responsive Safety in Reinforcement Learning by PID Lagrangian Methods" introduces an enhanced approach to address the safety concerns inherent in reinforcement learning (RL) environments using constrained optimization techniques. The authors propose a novel method based on Proportional-Integral-Derivative (PID) control principles applied to Lagrange multipliers, aiming to mitigate constraint oscillations and overshoots typically observed in safe reinforcement learning scenarios.

Key Contributions

1. PID Lagrangian Methods: The paper emphasizes the development of an advanced update mechanism for Lagrange multipliers employing PID control principles. Traditional Lagrange multiplier updates, which mirror integral control, are expanded to include proportional and derivative elements. This extension aims to provide better dynamic response, reducing oscillations and enhancing constraint satisfaction throughout the learning process. The inclusion of proportional control hastens response and dampens oscillations, while derivative control anticipates constraint violations, potentially preventing overshoot.
2. Experimental Validation: Experiments conducted in the OpenAI Safety-Gym suite illustrate the effectiveness of the proposed PID Lagrangian methods. The results show enhanced state-of-the-art performance by achieving robust safety compliance and reducing constraint violations across multiple environments, such as PointGoal and DoggoButton. These experiments underscore the methods' practicality and effectiveness in complex simulated scenarios involving various robotic locomotion tasks.
3. Control Efficiency and Robustness: The utilization of proportional and derivative control components not only stabilizes the learning dynamics but also alleviates the tuning and scale sensitivity associated with the traditional Lagrange multiplier update rules. The results demonstrate that the proposed PID controller provides a more control-efficient and robust performance across a range of learning rate settings.
4. Reward-Scale Invariance: An auxiliary contribution of the paper is the introduction of a technique to achieve reward-scale invariance by adjusting the policy gradient with a scale factor determined by the ratio of reward to cost gradients. This contributes significantly to making controller parameters more robust to changes in problem scale, thus facilitating the application and scalability of constrained RL across different task domains.

Implications and Future Directions

The advancements presented in this paper have significant implications for safe RL, particularly in domains requiring stringent adherence to safety constraints such as autonomous vehicles, drones, and healthcare. The PID Lagrangian method's ability to refine safety constraints dynamically allows RL algorithms to be applied more broadly while maintaining safety throughout the training process.

The potential for further exploration is substantial:

• An in-depth theoretical analysis of PID parameters could provide insights into optimal tuning strategies, enhancing the applicability of the method across varied domains.
• Extending PID control principles to multi-constrained or multi-agent RL environments could leverage the method's potential benefits in more complex scenarios.

Overall, the methodology presented in this paper has paved the way for improved safety in RL by coupling traditional control theory with modern machine learning, fostering a more robust and consistent approach to managing reinforcement learning in safety-critical environments.