This article presents an approach to constructing a valid and error-free career agent with Deep Reinforcement Learning (DRL).
Alongside the other methods, formal verification in the context of deep reinforcement learning would enable us to provide guarantees to the user over metrics ...
Mar 3, 2024 · Read 2023 featured article of FAC "A Deep Reinforcement Learning Framework with Formal Verification". It presents an approach to ...
Mar 3, 2024 · Read 2023 featured article of FAC "A Deep Reinforcement Learning Framework with Formal Verification". It presents an approach to ...
Abstract—We consider the problem of Safe Deep Reinforce- ment Learning (DRL) using formal verification in a trajectory generation task.
May 25, 2021 · In this work, we present the whiRL 2.0 tool, which implements a new approach for verifying complex properties of interest for DRL systems.
Deep Learning Verification (DLV) [95] is an automated verification framework based on Satisfiability. Modulo Theories (SMT). The algorithm focuses on single ...
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In this work, we introduce a Safe-DRL framework that incorporates safety constraints for the automation of surgical subtasks via DRL training.
The foundations of DRL and DRL verification are established, a taxonomy for D RL verification methods are defined, approaches for dealing with stochasticity ...