# A Multitask-Based Transfer Framework for Cooperative Multi-Agent Reinforcement Learning
## 2 Related Work  
### Cooperative Multi-Agent Reinforcement Learning  
In this section, we will review research approaches and the current state of the art in CMARL, highlighting existing methods and frameworks based on different environments. CMARL explores algorithms and strategies that facilitate collaborative learning, adaptation, and decision-making in shared environments by multiple agents. In a technologically driven world where multi-agent systems are becoming increasingly prevalent, ensuring effective and seamless cooperation between agents is of paramount importance. To address this, various research environments have been developed, each designed to test and refine the strategies and algorithms in CMARL. These environments provide distinct challenges, facilitating the exploration of collaborative behaviors, task allocation, and adaptation in dynamic settings. The authors in [18, 19, 20] build upon the cooperative video game environment Overcooked [21], where agents collaboratively prepare and serve onion soup by dividing tasks and adapting to varying teammates, focusing on scenarios in which agents must cooperate with new individuals they have not encountered during prior training, thereby exploring approaches for effective human-AI coordination in future applications. In [22, 23, 24], multi-agent cooperation is investigated in the MuJoCo Soccer environment [25], a simplified 2v2 team-based game where agents navigate a 3D space with the objective of coordinating to kick a ball into the opposing team's goal. Their work focuses on enhancing coordination, communication, and tactical behavior to improve performance and facilitate real-world applications in dynamic, team-based tasks. The StarCraft II Multi-Agent Challenge (SMAC) [8] and Google Research Football (GRF) [9] provide environments for the study of multi-agent cooperation. In the context of SMAC, agents collaborate in a real-time strategy environment where partial observability applies, while in GRF, they engage in dynamic collaborative play in a football game with the objective of controlling the ball and scoring goals. To narrow the gap between multi-agent systems and real-world applications, [PERSON] et al. [26] and [PERSON] et al. [27] increase environmental complexity and introduce challenges that promote deeper cooperation. Specifically, [PERSON] et al. [27] incorporates randomized elements such as varying unit types and spawn locations, whereas [PERSON] et al. [26] introduces hybrid opponent strategy scripts. In addition, [PERSON] et al. [28] improves training efficiency by adaptively selecting the most informative learning segments and explores ways to optimize information sharing and decision coordination. [PERSON] et al. [29] leverages contrastive learning and temporal attention mechanisms to mitigate negative transfer, accelerating agent adaptation to new environments. Meanwhile, [PERSON] et al. [30] presents a general training framework that tackles the curse of dimensionality by training on alternating subsets of agents, using existing deep MARL algorithms as base trainers. [PERSON] et al. [31] proposes the Dynamic Subtask Assignment framework, which dynamically allocates agents to subtasks based on their abilities using a subtask encoder and ability-based selection strategy, alongside regularizers to stabilize training and boost performance in both SMAC and GRF. [PERSON] et al. [32] introduce the dynamic skill learning framework, enabling agents to acquire diverse abilities through intrinsic rewards, which enhances performance in cooperative MARL tasks like SMAC and GRF. [PERSON] et al. [33] proposes a metric for evaluating cooperation between an ego-agent and unfamiliar partners in dynamic team compositions, emphasizing adaptability and generalization in collaborative scenarios. However, the aforementioned studies predominantly focus on multi-agent cooperation tasks within single environments and generally lack cross-domain experimental analysis, limiting the broader applicability of their findings.