Types of Multi-Agent Interactions
- Cooperative: Agents work together to achieve a common goal. Success depends on effective coordination and communication.
- Competitive: Agents are in direct competition, each trying to maximize their individual rewards often at the expense of others.
- Mixed: A combination of cooperation and competition where agents may form alliances but also face rivalry.
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.
Contact Us