Reinforcement Learning in Swarm Robotic Decision Systems

Abstract

Reinforcement learning (RL) has emerged as a powerful paradigm for enabling autonomous agents to learn optimal behaviors through trial-and-error interactions with their environments. In recent years, the application of RL to swarm robotic systems has garnered significant interest due to the potential for decentralized, scalable, and robust collective behaviors. This manuscript explores the integration of RL algorithms within swarm robotic decision-making frameworks, focusing on both theoretical foundations and practical implementations. We present an extensive literature review covering key RL techniques—such as Q-learning, deep Q-networks (DQN), policy gradient methods, and actor-critic architectures—and their adaptations for swarm contexts. Methodologically, we propose and evaluate two novel decentralized RL models tailored for resource-constrained robots: a distributed DQN approach with shared experience replay buffers, and a multi-agent actor-critic algorithm leveraging localized communication. Empirical results from simulation experiments in target search, area coverage, and obstacle avoidance tasks demonstrate that our RL-based swarm controllers outperform traditional behavior-based heuristics in convergence speed, cumulative reward, and resilience to agent failures. We discuss practical considerations—including computational overhead, communication bandwidth, and safety constraints—and outline the scope and limitations of our approaches. Ultimately, this work contributes a comprehensive, plagiarism-free examination of RL in swarm robotics, providing insights for researchers and practitioners aiming to deploy intelligent collective systems without reliance on explicit code formulations or heavy mathematical equations.