Search Results for author:
Found 4 papers, 1 papers with code
Reinforcement Learning with Adaptive Control Regularization for Safe Control of Critical Systems
Reinforcement Learning (RL) is a powerful method for controlling dynamic systems, but its learning mechanism can lead to unpredictable actions that undermine the safety of critical systems.
Feedback control for distributed ledgers: An attack mitigation policy for DAG-based DLTs
In this paper we present a feedback approach to the design of an attack mitigation policy for DAG-based Distributed Ledgers.
Driving Reinforcement Learning with Models
In this paper we propose a new approach to complement reinforcement learning (RL) with model-based control (in particular, Model Predictive Control - MPC).
Spatial Positioning Token (SPToken) for Smart Mobility
We introduce a permissioned distributed ledger technology (DLT) design for crowdsourced smart mobility applications.
Cryptography and Security
