Parallel Reinforcement Learning With Minimal Communication Overhead for IoT Environments.

Many Internet of Things (IoT) applications require a distributed architecture for decision making either because of a lack of a centralized system, failure-prone connectivity to a centralized system or because the imposed latency to contact such a system is too high for real-time applications. Often, these IoT applications fall in the domain of reinforcement learning (RL), e.g., autonomous robot n...