Last updated on Jul 1, 2024

How do you foster collaboration and coordination among multiple agents with different goals and preferences?

Reinforcement learning (RL) is a branch of artificial intelligence that studies how agents learn from their own actions and rewards in an environment. RL can be applied to various domains, such as robotics, games, and social dilemmas. However, what if there are multiple agents in the same environment, each with their own goals and preferences? How can they collaborate and coordinate with each other to achieve a common or individual objective? This is the challenge of multi-agent reinforcement learning (MARL), which extends the RL framework to account for the interactions and influences among multiple agents. In this article, you will learn about some of the key concepts and methods of MARL, such as types of games, learning algorithms, and communication protocols.

1 Types of games

A game is a formal model of a situation where multiple agents have conflicting or cooperative interests. Depending on the nature of the agents' goals and preferences, games can be classified into different types, such as zero-sum, cooperative, competitive, or mixed-motive. For example, in a zero-sum game, the agents' rewards are inversely proportional, meaning that one agent's gain is another agent's loss. In a cooperative game, the agents share a common reward function and work together to maximize it. In a competitive game, the agents have opposite reward functions and try to minimize each other's rewards. In a mixed-motive game, the agents have both cooperative and competitive aspects in their reward functions, creating a trade-off between individual and collective outcomes.

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Aju Ani Justus

PhD Researcher // Machine Learning Engineer // Reinforcement Learning, Human-Agent Teaming
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Something that helped me when I started working on multi-agent systems was associating different types of games to video games I love. In Overcooked, where you're rushing to cook meals together (cooperative games). In a competitive match in Valorant you're planning to outsmart opponents (competitive games). In Civilization-like games, you're making deals and competing for resources (mixed-motive games). Finally, in a Battle Royale, it's about survival and trickery, where alliances form and betrayals happen and winner takes it all (zero-sum games). Connecting traditional game theory or mathematical games (like stag hunt or prisoner's dilemma) to games I enjoy helps me think more creatively when developing agents in multi-agent systems. 🎮

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2 Learning algorithms

Learning algorithms are the methods that agents use to update their policies or strategies based on their experiences and rewards. In MARL, learning algorithms can be categorized into two main types: centralized and decentralized. In centralized learning, there is a central controller or observer that has access to the global information and coordinates the learning process of all agents. In decentralized learning, each agent learns independently and locally, based on its own observations and actions. Centralized learning can achieve better performance and coordination, but it requires more communication and computation resources. Decentralized learning can be more scalable and robust, but it faces challenges such as non-stationarity, partial observability, and coordination.

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3 Communication protocols

Communication protocols are the rules or mechanisms that agents use to exchange information or signals with each other. Communication can enhance the collaboration and coordination among agents, by allowing them to share their beliefs, intentions, or actions. However, communication also introduces costs and constraints, such as bandwidth, noise, or privacy. Therefore, designing effective and efficient communication protocols is a crucial aspect of MARL. Communication protocols can be classified into two main types: explicit and implicit. In explicit communication, agents send and receive messages through a predefined language or code. In implicit communication, agents infer information from each other's behaviors or actions, without using a specific language or code.

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