Cooperation vs. Competition
In MARL, agents can exhibit cooperative, competitive, or mixed behaviors, depending on the nature of their interactions and objectives.
- Cooperative MARL: Multi-agent systems involve agents that coordinate with one another to accomplish the task and even share profits and find the optimal policy for the overall system. This is similar to group tasks for which the collective performance decides the success. Such machines include cobots or industrial robots in manufacturing or search and rescue robots in search-and-rescue missions.
- Competitive MARL: In this case, agents have conflicting objectives because agents are self-interested in maximizing rewards for themselves and minimizing the rewards of other agents. This is characteristic to zero-sum games such as chess or poker in which a gain by one participating agent is a loss by another.
- Mixed MARL: The study presented real life situations can entail cooperative and competitive features. For instance, self-driving cars may have to collaborate to ensure they do not hit each other but compete for the best route so that they can have minimum travel time.
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.