Reinforcement Learning for Embedded AI & Swarm Systems

Synopsis

Authored by Vikram Singh Sankhala, "Reinforcement Learning for Embedded AI & Swarm Systems" is an essential guide for engineers and developers seeking to build, deploy, and scale intelligent, protocol-driven systems. The book delves into the practical application of Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) within the constraints of embedded AI and the complexities of swarm robotics. It starts with foundational concepts, including MDPs, Q-Learning, and Policy Gradients, progressing through Deep RL basics like DQN and Actor-Critic architectures, while immediately addressing edge-specific challenges such as sample efficiency and partial observability in resource-constrained environments. The narrative quickly moves to advanced topics, demonstrating how RL can optimize embedded Small Language Models (SLMs), enable dynamic task allocation, and drive multi-agent coordination in swarms using decentralized techniques and communication-efficient MARL.