Preoperative localization of small peripheral pulmonary nodules using CT-guided embolization coil insertion and subsequent fluoroscopically-guided VATS resection is safer and more effective than conventional VATS.

Utilizing messages from teammates is crucial in cooperative multi-agent tasks due to the partially observable nature of the environment. Naively asking messages from all teammates without pruning may confuse individual agents, hindering the learning process and impairing the whole system's performance. Most previous work either utilizes a gate or employs an attention mechanism to extract relatively important messages. However, they do not explicitly evaluate each message's value, failing to learn an efficient communication protocol in more complex scenarios. To tackle this issue, we model the teammates of an agent as a message coalition and calculate the Shapley Message Value (SMV) of each agent within it. SMV reflects the contribution of each message to an agent and redundant messages can be spotted in this way effectively. On top of that, we design a novel framework named Shapley Message Selector (SMS), which learns to predict the SMVs of teammates for an agent solely based on local information so that the agent can only query those teammates with positive SMVs. Empirically, we demonstrate that our method can prune redundant messages and achieve comparable or better performance in various multi-agent cooperative scenarios than full communication settings and existing strong baselines.

Value-based multi-agent reinforcement learning (MARL) methods hold the promise of promoting coordination in cooperative settings. Popular MARL methods mainly focus on the scalability or the representational capacity of value functions. Such a learning paradigm can reduce agents' uncertainties and promote coordination. However, they fail to leverage the task structure decomposability, which generally exists in real-world multi-agent systems (MASs), leading to a significant amount of time exploring the optimal policy in complex scenarios. To address this limitation, we propose a novel framework Multi-Agent Concentrative Coordination (MACC) based on task decomposition, with which an agent can implicitly form local groups to reduce the learning space to facilitate coordination. In MACC, agents first learn representations for subtasks from their local information and then implement an attention mechanism to concentrate on the most relevant ones. Thus, agents can pay targeted attention to specific subtasks and improve coordination. Extensive experiments on various complex multi-agent benchmarks demonstrate that MACC achieves remarkable performance compared to existing methods.