Abstract
Reinforcement Learning (RL) has emerged as a powerful paradigm in artificial intelligence, enabling agents to learn optimal behaviors through interaction with dynamic environments. Traditional RL focuses on single-agent scenarios where an agent maximizes cumulative rewards through trial-and-error learning. However, real-world applications often involve multiple interacting agents, leading to the development of Multi-Agent Reinforcement Learning (MARL). MARL introduces challenges such as non-stationarity, coordination, and scalability, demanding innovative solutions and learning frameworks. This paper provides a comprehensive review of RL and MARL, discussing fundamental principles, key algorithms, theoretical foundations, practical applications, and ongoing challenges. Furthermore, it examines recent advancements in MARL strategies, including decentralized learning, cooperative-competitive frameworks, and emergent behaviors. The paper also highlights promising applications across autonomous systems, robotics, smart grids, and traffic management, emphasizing the transformative potential of RL and MARL in complex, dynamic environments.
Keywords: Reinforcement Learning, Multi-Agent Reinforcement Learning, Q-Learning, Policy Gradient, Deep RL, Cooperative Agents, Competitive Agents, Game Theory, Autonomous Systems
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