An Introduction to Decentralized Training and Execution in Cooperative Multi-Agent Reinforcement Learning
<|reference_start|>An Introduction to Decentralized Training and Execution in Cooperative Multi-Agent Reinforcement Learning: Multi-agent reinforcement learning (MARL) has exploded in popularity in recent years. Many approaches have been developed but they can be divided into three main types: centralized training and execution (CTE), centralized training for decentralized execution (CTDE), and Decentralized training and execution (DTE). Decentralized training and execution methods make the fewest assumptions and are often simple to implement. In fact, as I'll discuss, any single-agent RL method can be used for DTE by just letting each agent learn separately. Of course, there are pros and cons to such approaches. It is worth noting that DTE is required if no offline coordination is available. That is, if all agents must learn during online interactions without prior coordination, learning and execution must both be decentralized. DTE methods can be applied in cooperative, competitive, or mixed cases but this text will focus on the cooperative MARL case.   This text is an introduction to the field of decentralized, cooperative MARL. As such, I will first give a brief description of the cooperative MARL problem in the form of the Dec-POMDP. Then, I will discuss value-based DTE methods starting with independent Q-learning and its extensions and then discuss the extension to the deep case with DQN, the additional complications this causes, and methods that have been developed to (attempt to) address these issues. Next, I will discuss policy gradient DTE methods starting with independent REINFORCE (i.e., vanilla policy gradient), and then extending to the actor-critic case and deep variants (such as independent PPO). Finally, I will discuss some general topics related to DTE and future directions.<|reference_end|>
@article{amato2024an,
    title={An Introduction to Decentralized Training and Execution in Cooperative
  Multi-Agent Reinforcement Learning},
    author={Christopher Amato},
    journal={arXiv preprint arXiv:2405.06161},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06161},
    primaryClass={cs.LG cs.MA}
}