Multi-Agent Reinforcement Learning (MARL) System
A Multi-Agent Reinforcement Learning (MARL) System is a reinforcement learning system that can be used to create multi-agent learning systems that can support cooperative or competitive decision-making tasks.
- Context:
- Context:
- It can coordinate the behavior of multiple autonomous agents interacting in a shared environment over time.
- It can support cooperative tasks by enabling agents to share information and learn joint policies.
- It can support competitive tasks by enabling agents to model and respond to adversaries.
- It can implement centralized training with decentralized execution to balance coordination and autonomy.
- It can integrate communication protocols between agents for improved strategy formation.
- It can employ value decomposition or policy factorization methods for scalable multi-agent credit assignment.
- It can incorporate divergence-based regularization (e.g., maximum mean discrepancy) to align latent representations or value functions across agents.
- It can leverage maximum mean discrepancy as a functional regularizer to promote representation alignment and coordination among decentralized policy or value networks.
- It can range from using simple tabular Q-learning extensions to using deep, continuous-space policy gradient architectures.
- It can range from systems with full observability and cooperation to partially observable, adversarial multi-agent environments.
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- Example(s):
- MADDPG-based systems, which support continuous multi-agent coordination using actor-critic networks.
- QMIX-based systems, which apply value function factorization for cooperative MARL in discrete domains.
- MMD-MIX systems, which embed maximum mean discrepancy to regularize alignment across agents’ value networks.
- Multi-agent robotic systems coordinating in warehouse navigation or search-and-rescue scenarios.
- Competitive AI agents trained for real-time strategy games using multi-agent reinforcement learning.
- ...
- Counter-Example(s):
- Single-Agent Reinforcement Learning System, which handles isolated agent training without inter-agent interactions.
- Multi-Agent Planning System, which coordinates agents through explicit planning and search rather than learning.
- Rule-Based Multi-Agent System, which uses hand-crafted logic instead of reinforcement-based optimization.
- ...
- See: Reinforcement Learning System, Multi-Agent Learning System, Deep Reinforcement Learning Algorithm, Value Decomposition, MADDPG, QMIX, MMD-MIX.
References
2021
- (Yang et al., 2021) ⇒ Yifan Yang, Fengda Zhang, Yali Du, Hang Su, and Jun Zhu (2021). "MMD-MIX: Multi-Agent Mix with Maximum Mean Discrepancy for Efficient Coordination in MARL". In: arXiv:2106.11652 [cs.LG].
- QUOTE: MMD-MIX is a multi-agent reinforcement learning method that introduces a maximum mean discrepancy regularizer into value function decomposition.
The regularizer promotes alignment among agent-specific Q-functions while maintaining representational diversity.
Empirical results demonstrate improved coordination and sample efficiency across a variety of multi-agent benchmarks.
- QUOTE: MMD-MIX is a multi-agent reinforcement learning method that introduces a maximum mean discrepancy regularizer into value function decomposition.
2018
- (Rashid et al., 2018) ⇒ Tabish Rashid, Mikayel Samvelyan, Christian Schroeder de Witt, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson (2018). "QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning". In: Proceedings of the 35th International Conference on Machine Learning (ICML).
- QUOTE: QMIX proposes a monotonic mixing network for combining individual agent Q-values into a global joint action-value function.
This structure enables centralized training with decentralized execution and supports cooperative learning.
Extensive experiments on the StarCraft II Micromanagement Benchmark validate its performance over existing MARL approaches.
- QUOTE: QMIX proposes a monotonic mixing network for combining individual agent Q-values into a global joint action-value function.
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: The authors introduce MADDPG, a centralized training framework using actor-critic networks for multi-agent reinforcement learning.
Agents are trained with access to all observations and actions but execute policies independently at test time.
This approach enables stable learning in both cooperative and competitive settings.
- QUOTE: The authors introduce MADDPG, a centralized training framework using actor-critic networks for multi-agent reinforcement learning.
2004
- (Panait & Luke, 2004) ⇒ Liviu Panait, and Sean Luke (2004). "Cooperative Multi-Agent Learning: The State of the Art". In: Autonomous Agents and Multi-Agent Systems.
- QUOTE: This survey reviews a wide range of techniques for cooperative multi-agent learning, including reinforcement learning, game theory, and evolutionary methods.
It identifies key challenges such as credit assignment, non-stationarity, and scalability.
The paper highlights early trends that have influenced the development of modern MARL systems.
- QUOTE: This survey reviews a wide range of techniques for cooperative multi-agent learning, including reinforcement learning, game theory, and evolutionary methods.