Pure-Past Action Masking

We present Pure-Past Action Masking (PPAM), a lightweight approach to action masking for safe reinforcement learning. In PPAM, actions are disallowed (“masked”) according to specifications expressed in Pure-Past Linear Temporal Logic (PPLTL). PPAM can enforce non-Markovian constraints, i.e., constraints based on the history of the system, rather than just the current state of the (possibly hidden) MDP. The features used in the safety constraint need not be the same as those used by the learning agent, allowing a clear separation of concerns between the safety constraints and reward specifications of the (learning) agent. We prove formally that an agent trained with PPAM can learn any optimal policy that satisfies the safety constraints, and that they are as expressive as shields, another approach to enforce non-Markovian constraints in RL. Finally, we provide empirical results showing how PPAM can guarantee constraint satisfaction in practice

Pure-past action masking

We present Pure-Past Action Masking (PPAM), a lightweight approach to action masking for safe reinforcement learning. In PPAM, actions are disallowed (“masked”) according to specifications expressed in Pure-Past Linear Temporal Logic (PPLTL). PPAM can enforce non-Markovian constraints, i.e., constraints based on the history of the system, rather than just the current state of the (possibly hidden) MDP. The features used in the safety constraint need not be the same as those used by the learning agent, allowing a clear separation of concerns between the safety constraints and reward specifications of the (learning) agent. We prove formally that an agent trained with PPAM can learn any optimal policy that satisfies the safety constraints, and that they are as expressive as shields, another approach to enforce non-Markovian constraints in RL. Finally, we provide empirical results showing how PPAM can guarantee constraint satisfaction in practice

Distributed Graph Queries for Runtime Monitoring of Cyber-Physical Systems

In safety-critical cyber-physical systems (CPS), a service failure may result in severe financial loss or damage in human life. Smart CPSs have complex interaction with their environment which is rarely known in advance, and they heavily depend on intelligent data processing carried out over a heterogeneous computation platform and provide autonomous behavior. This complexity makes design time verification infeasible in practice, and many CPSs need advanced runtime monitoring techniques to ensure safe operation. While graph queries are a powerful technique used in many industrial design tools of CPSs, in this paper, we propose to use them to specify safety properties for runtime monitors on a high-level of abstraction. Distributed runtime monitoring is carried out by evaluating graph queries over a distributed runtime model of the system which incorporates domain concepts and platform information. We provide a semantic treatment of distributed graph queries using 3-valued logic. Our approach is illustrated and an initial evaluation is carried out using an educational demonstrator of CPSs

Fundamental Approaches to Software Engineering

computer software maintenance; computer software selection and evaluation; formal logic; formal methods; formal specification; programming languages; semantics; software engineering; specifications; verificatio