# A Multitask-Based Transfer Framework for Cooperative Multi-Agent Reinforcement Learning
## 2 Related Work
### Multitask Learning  
Here, we will provide a review of existing solutions for multitask learning (MTL) [34; 35; 36], particularly in the context of reinforcement learning (RL). MTL, a method that follows a \"learning-to-learn\" approach to utilize a system's shared representations to train, in parallel, the domain-relevant information accumulated from individually relevant tasks, has become a state-of-the-art learning method for many benchmark tasks and even certain real-world problems. This ability to leverage shared representations has been particularly effective in domains such as natural language processing (NLP) [37] and computer vision (CV) [38], where a wide range of tasks often share underlying structures or features. Just as \"Jade can be polished with stones from other mountains\". Researchers [39; 40; 41] have also demonstrated the effectiveness of MTL in reinforcement learning, a task known as multitask reinforcement learning (MTRL).  
Moreover, attention-based mechanisms [29] have been incorporated into MTRL to selectively focus on task-relevant features, improving knowledge transfer and task performance. The authors in [42; 43] focus on solving gradient challenges in multitask learning, especially in optimizing task-specific gradients and dealing with conflicting gradients between tasks. The works [44; 45] can be classified as routing-based methods, where different strategies are employed to allocate resources (e.g., encoders, modules, or parameters) across tasks based on their specific requirements or difficulty levels.  
However, the MTRL studies mentioned above are all based on a single-agent environment, which we refer to as multitask single-agent reinforcement learning (MT-SARL). Although MT-SARL research has made significant progress, it still faces significant challenges when applied to real-world problems, which often involve complex and dynamic tasks. Furthermore, the limitations of the single-agent framework in MT-SARL hinder its ability to effectively manage the interactions and diverse tasks present in more realistic multi-agent settings. These challenges highlight the growing need for multitask multi-agent reinforcement learning (MT-MARL), where multiple agents must simultaneously collaborate, compete, and adapt to various tasks, reflecting the complexity of real-world scenarios.  
Recent research in MT-MARL has made significant progress in overcoming key challenges, focusing on enhancing knowledge sharing, coordination, and efficient resource allocation among agents. [PERSON] et al. [46] address these challenges by employing pre-trained language models for task encoding, utilizing a task entity transformer to handle varying agent numbers and task complexity, and optimizing training efficiency through task learning weight adjustments based on task regret. [PERSON] et al. [47] introduce a novel MT-MARL algorithm that improves performance by modeling agent interactions via interactive value decomposition and learning task representations from local trajectories, facilitating knowledge transfer across related tasks in both multitask and zero-shot scenarios. [PERSON] et al. [48] proposes a new multitask approach for cooperative MARL, leveraging knowledge transfer from task-specific teachers to enable a single team of agents to achieve expert-level performance across multiple tasks. This method uses a knowledge distillation algorithm tailored for multi-agent architectures and incorporates teacher annealing, which gradually shifts learning from distillation to environmental rewards, enabling the multitask model to surpass its singletask counterparts. In contrast to the approach in [48], which requires a two-step process of pre-training a teacher model for each task before learning, our method simplifies the process by starting multitask training directly from scratch without the need for a teacher model.