Social Dilemmas in MARL
Social dilemmas, such as the prisoner’s dilemma and chicken, present challenges where individual interests conflict with collective outcomes. In Multi-Agent Reinforcement Learning (MARL), understanding and addressing these dilemmas are crucial. MARL approaches social dilemmas by exploring how agents can learn to navigate them through trial-and-error processes. Balancing individual incentives with collective welfare is a central challenge, prompting research into techniques for promoting cooperation among agents.
Real-world scenarios often involve Sequential Social Dilemmas (SSDs), where agents make decisions over time, adding complexity to the dynamics of cooperation. By leveraging reinforcement learning and exploring novel approaches, MARL aims to foster cooperative behavior in multi-agent systems.
Multi-Agent Reinforcement Learning in AI
Reinforcement learning (RL) can solve complex problems through trial and error, learning from the environment to make optimal decisions. While single-agent reinforcement learning has made remarkable strides, many real-world problems involve multiple agents interacting within the same environment. This is where multi-agent reinforcement learning (MARL) comes into play, offering a framework for agents to learn, collaborate, and compete, thereby enhancing their collective performance.
This article delves into the concepts, challenges, and applications of Multi-Agent Reinforcement Learning (MARL) in AI.
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