Adaptive Trust Consensus for Blockchain IoT: Comparing RL, DRL, and MARL Against Naive, Collusive, Adaptive, Byzantine, and Sleeper Attacks

The article focuses on a research paper comparing different reinforcement learning (RL) techniques (RL, DRL, MARL) for building a more robust trust consensus mechanism in the context of Blockchain-based Internet of Things (IoT) systems. The research aims to defend against various attack types. The title clearly indicates the scope and the methodology of the research.

• •The research explores the application of RL, DRL, and MARL in blockchain IoT.
• •The study aims to improve trust consensus mechanisms.
• •The research addresses various attack vectors in IoT systems.