Safe Reinforcement Learning via Shielding (1708.08611v2)

Abstract: Reinforcement learning algorithms discover policies that maximize reward, but do not necessarily guarantee safety during learning or execution phases. We introduce a new approach to learn optimal policies while enforcing properties expressed in temporal logic. To this end, given the temporal logic specification that is to be obeyed by the learning system, we propose to synthesize a reactive system called a shield. The shield is introduced in the traditional learning process in two alternative ways, depending on the location at which the shield is implemented. In the first one, the shield acts each time the learning agent is about to make a decision and provides a list of safe actions. In the second way, the shield is introduced after the learning agent. The shield monitors the actions from the learner and corrects them only if the chosen action causes a violation of the specification. We discuss which requirements a shield must meet to preserve the convergence guarantees of the learner. Finally, we demonstrate the versatility of our approach on several challenging reinforcement learning scenarios.

• The paper introduces shielding as a systematic method to ensure RL agents adhere to temporal logic safety requirements.
• It details two implementations—preemptive shielding restricting actions and post-posed shielding correcting actions—to maintain safety.
• Empirical results demonstrate that shielded agents learn faster and converge to optimal policies without recurring safety violations.

Safe Reinforcement Learning via Shielding

The paper "Safe Reinforcement Learning via Shielding" introduces a novel approach to integrating safety guarantees into reinforcement learning (RL) by employing formal methods. The authors propose the concept of shielding, a mechanism that ensures learned policies comply with pre-specified safety properties expressed in temporal logic. This approach addresses the critical need for safety in RL applications, particularly in environments where agents operate near humans or in dynamic contexts.

Approach and Methodology

The core idea is to implement a shield—an automated system that supervises the RL agent's actions—ensuring compliance with temporal logic safety specifications. The paper suggests two primary implementations of the shield:

1. Preemptive Shielding: The shield restricts the agent's actions to a set of pre-validated safe actions before the agent makes a decision. This implementation guarantees that the agent only explores actions deemed safe according to the temporal logic criteria defined upfront.
2. Post-Posed Shielding: Here, the shield monitors and, if necessary, corrects the agent's actions after selection but before execution. This approach allows the shield to override unsafe actions dynamically, providing flexibility in scenarios where the learning agent might not be aware of the safety constraints inherently.

The shield's synthesis leverages a combination of game theory and formal verification techniques. Specifically, a safety game is constructed from the safety logic specifications and an abstraction of the environment. Solving this game provides a strategy ensuring that the shield satisfies the safety requirements.

Numerical Results and Implications

The paper presents several empirical test cases, including grid-world tasks, a driving simulation, and an Atari game scenario. Key findings indicate that:

• Shielded agents demonstrated superior learning efficiency compared to unshielded counterparts, with faster convergence to optimal or near-optimal policies.
• The shield effectively prevents safety violations without notable adverse impacts on the learning process.
• In some scenarios, shielding accelerated learning by guiding the exploration process towards safer regions of the action space.

Theoretical and Practical Implications

The introduction of formal methods into RL using shielding opens up a significant avenue for deploying RL-based systems in safety-critical applications. The use of temporal logic specifications ensures that safety properties can be specified rigorously, and the shield's design ensures that these properties are adhered to during both the learning and execution phases.

Practically, this enables the deployment of RL agents in domains like autonomous driving and robotic control, where safety is paramount. The framework also allows incorporation with complex function approximators like deep neural networks, maintaining scalability across large and continuous state spaces.

Future Directions

The paper sets the stage for further exploration in several areas:

• Scalability: As RL environments grow in complexity, ensuring that the abstraction and specification processes scale efficiently is crucial.
• Hierarchical Shielding: Exploring layered approaches where multiple shields are employed to manage different levels of safety and operational objectives.
• Cross-Domain Applications: Adapting and validating this framework in diverse fields that combine safety-critical requirements with intelligent control, such as healthcare or industrial automation.

In conclusion, by combining reinforcement learning with formal safety guarantees through shielding, this approach significantly advances the potential deployment of RL in real-world, safety-critical situations. The paper presents a well-founded methodology that marries theoretical rigor with practical applicability, addressing one of the core challenges inhibiting widespread RL adoption.