Multi-Agent Learning (MAL) System
A Multi-Agent Learning (MAL) System is a learning system that can be used to create multi-agent interaction frameworks that can support cooperative or competitive tasks among autonomous agents.
- Context:
- It can enable multiple autonomous agents to learn behaviors or strategies through repeated interactions in a shared or decentralized environment.
- It can support cooperative tasks, where agents aim to maximize a shared reward or achieve joint goals.
- It can support competitive tasks, where agents pursue conflicting objectives or operate under adversarial dynamics.
- It can use reinforcement learning algorithms such as independent Q-learning, WoLF-PHC, or MADDPG to model agent policy updates over time.
- It can apply centralized or decentralized learning architectures depending on observability, communication constraints, and coordination requirements.
- It can address challenges such as non-stationarity, credit assignment, and scalability in multi-agent environments.
- It can implement mechanisms like opponent modeling, joint action learners, or policy-space reasoning to improve convergence and stability.
- It can incorporate divergence regularization techniques, such as maximum mean discrepancy, to align policies or representations between learning agents.
- It can range from systems operating in fully observable simulated environments to those used in real-world robotic or economic simulations.
- It can range from being a Simple Multi-Agent Learning System to being a Multi-Agent Reinforcement Learning System.
- It can range from being a Cooperative Multi-Agent Learning System to being a Competitive Multi-Agent Learning System.
- ...
- Example(s):
- Independent Q-Learning Systems, where each agent learns its own value function while treating others as part of the environment.
- Win or Learn Fast (WoLF) Systems, which adjust learning rates based on success to adapt quickly in adversarial dynamics.
- MADDPG Systems that use actor-critic models with centralized critics to stabilize multi-agent policy learning.
- AWESOME Systems, which alternate between optimal best response and modeling equilibrium strategies in repeated stochastic games.
- ECMLA Systems, which use ensemble-based coordination to dynamically select among multiple learning algorithms based on environment characteristics.
- LoE-AIM Systems, which learn opponent models in adversarial environments through learning-on-the-fly mechanisms and action inference.
- ReDVaLeR Systems, which incorporate deep reinforcement learning with risk-aware decision-making to handle uncertainty in multi-agent collaboration.
- Weighted Policy Learners (WPL) Systems, which scale policy gradients in proportion to convergence confidence to ensure smooth policy adaptation in multi-agent games.
- Cooperative robot swarm learning systems that learn to navigate and allocate tasks collectively.
- Competitive multi-agent economic simulation platforms that model strategic behavior among rational agents.
- ...
- Counter-Example(s):
- Single-Agent Learning System, which does not involve agent-to-agent interaction or coordination.
- Rule-Based Multi-Agent System, which follows fixed rules without adaptive learning from experience.
- Centralized Planning System, which solves multi-agent problems without distributed learning or policy updates.
- ...
- See: Multi-Agent Learning Task, Multi-Agent Reinforcement Learning System, Win or Learn Fast (WoLF) Algorithm, MADDPG, Opponent Modeling, Non-Stationarity in MARL, Learning Rate, ABM System, Nash Equilibrium.
References
2021
- (Papoudakis et al., 2021) ⇒ Georgios Papoudakis, Filippos Christianos, Christian Schroeder de Witt, and Stefanos Nikolaidis (2021). "Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks". In: arXiv:2006.07869 [cs.LG].
- QUOTE: The paper presents a comprehensive evaluation of multi-agent reinforcement learning systems across cooperative benchmark environments.
It compares algorithms such as MADDPG, QMIX, and COMA in terms of scalability, credit assignment, and sample efficiency.
The results highlight limitations in generalization and the impact of non-stationarity.
- QUOTE: The paper presents a comprehensive evaluation of multi-agent reinforcement learning systems across cooperative benchmark environments.
2019
- (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Multi-agent_system Retrieved:2019-2-3.
- A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning.
Despite considerable overlap, a multi-agent system is not always the same as an agent-based model (ABM). The goal of an ABM is to search for explanatory insight into the collective behavior of agents (which don't necessarily need to be "intelligent") obeying simple rules, typically in natural systems, rather than in solving specific practical or engineering problems. The terminology of ABM tends to be used more often in the sciences, and MAS in engineering and technology.[1] Applications where multi-agent systems research may deliver an appropriate approach include online trading, disaster response and social structure modelling.
- A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning.
2017
- (Lowe et al., 2017) ⇒ Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mordatch (2017). "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments". In: Advances in Neural Information Processing Systems (NeurIPS).
- QUOTE: MADDPG introduces centralized training with decentralized execution for multi-agent learning systems in both cooperative and competitive settings.
The method uses individual policy networks and a shared critic conditioned on all agents’ observations and actions.
Experiments show improved learning stability over independent learners.
- QUOTE: MADDPG introduces centralized training with decentralized execution for multi-agent learning systems in both cooperative and competitive settings.
2004
- (Bowling & Veloso, 2004) ⇒ Michael Bowling, and Manuela Veloso (2004). "Empirical Evaluation of Win or Learn Fast Policy Hill-Climbing". In: Proceedings of the 18th AAAI Conference on Artificial Intelligence.
- QUOTE: The WoLF-PHC algorithm adapts an agent's learning rate depending on whether it is performing better or worse than average.
This dynamic adjustment accelerates convergence and avoids instability in multi-agent environments.
The approach is evaluated in both cooperative and competitive game theoretic settings.
- QUOTE: The WoLF-PHC algorithm adapts an agent's learning rate depending on whether it is performing better or worse than average.
2003
- (Stone & Veloso, 2000) ⇒ Peter Stone, and Manuela Veloso (2000). "Multiagent Systems: A Survey from a Machine Learning Perspective". In: Autonomous Agents and Multi-Agent Systems.
- QUOTE: This survey outlines the core dimensions of multi-agent learning systems, including cooperation, competition, communication, and adaptation.
It reviews representative algorithms and highlights key research challenges such as credit assignment, scalability, and policy convergence.
The work offers a framework for classifying and evaluating MAL approaches.
- QUOTE: This survey outlines the core dimensions of multi-agent learning systems, including cooperation, competition, communication, and adaptation.
1999
- (Claus & Boutilier, 1999) ⇒ Caroline Claus, and Craig Boutilier (1999). "The Dynamics of Reinforcement Learning in Cooperative Multiagent Systems". In: Proceedings of the 15th National Conference on Artificial Intelligence (AAAI).
- QUOTE: The authors explore convergence properties of independent Q-learning in cooperative multi-agent settings.
They identify conditions under which convergence fails due to non-stationarity introduced by concurrently learning agents.
The study motivates the need for coordination mechanisms and policy regularization in MAL.
- QUOTE: The authors explore convergence properties of independent Q-learning in cooperative multi-agent settings.