In cooperative multi-agent reinforcement learning, centralized training and decentralized execution (CTDE) has achieved remarkable success. Individual Global Max (IGM) decomposition, which is an important element of CTDE, measures the consistency between local and joint policies. The majority of IGM-based research focuses on how to establish this consistent relationship, but little attention has been paid to examining IGM's potential flaws. In this work, we reveal that the IGM condition is a lossy decomposition, and the error of lossy decomposition will accumulated in hypernetwork-based methods. To address the above issue, we propose to adopt an imitation learning strategy to separate the lossy decomposition from Bellman iterations, thereby avoiding error accumulation. The proposed strategy is theoretically proved and empirically verified on the StarCraft Multi-Agent Challenge benchmark problem with zero sight view. The results also confirm that the proposed method outperforms state-of-the-art IGM-based approaches.

Summary and Contributions: Based on rebuttal and discussion: Upon reading all reviews, I recognize that we agree the article is well presented, and I stand by the concerns I raised. Note that I primarily criticized the absence of some relevant context in the original submission (which the authors admit in their rebuttal), rather than the contribution itself (albeit it may be smaller than proclaimed). Their refutation of it being a planning setting is fair. While I maintain that it is a self-play setting, this is implied by CTDE and thus not necessary to state again. A stale flavor remains from overselling their contribution's novelty in the introduction [L36-45].

In cooperative multi-agent reinforcement learning, centralized training and decentralized execution (CTDE) has achieved remarkable success. Individual Global Max (IGM) decomposition, which is an important element of CTDE, measures the consistency between local and joint policies. The majority of IGM-based research focuses on how to establish this consistent relationship, but little attention has been paid to examining IGM's potential flaws. In this work, we reveal that the IGM condition is a lossy decomposition, and the error of lossy decomposition will accumulated in hypernetwork-based methods. To address the above issue, we propose to adopt an imitation learning strategy to separate the lossy decomposition from Bellman iterations, thereby avoiding error accumulation.

The rapid advancement of multi-agent reinforcement learning (MARL) has given rise to diverse training paradigms to learn the policies of each agent in the multi-agent system. The paradigms of decentralized training and execution (DTDE) and centralized training with decentralized execution (CTDE) have been proposed and widely applied. However, as the number of agents increases, the inherent limitations of these frameworks significantly degrade the performance metrics, such as win rate, total reward, etc. To reduce the influence of the increasing number of agents on the performance metrics, we propose a novel training paradigm of grouped training decentralized execution (GTDE). This framework eliminates the need for a centralized module and relies solely on local information, effectively meeting the training requirements of large-scale multi-agent systems. Specifically, we first introduce an adaptive grouping module, which divides each agent into different groups based on their observation history. To implement end-to-end training, GTDE uses Gumbel-Sigmoid for efficient point-to-point sampling on the grouping distribution while ensuring gradient backpropagation. To adapt to the uncertainty in the number of members in a group, two methods are used to implement a group information aggregation module that merges member information within the group. Empirical results show that in a cooperative environment with 495 agents, GTDE increased the total reward by an average of 382\% compared to the baseline. In a competitive environment with 64 agents, GTDE achieved a 100\% win rate against the baseline.

Efficient collaboration in the centralized training with decentralized execution (CTDE) paradigm remains a challenge in cooperative multi-agent systems. We identify divergent action tendencies among agents as a significant obstacle to CTDE's training efficiency, requiring a large number of training samples to achieve a unified consensus on agents' policies. This divergence stems from the lack of adequate team consensus-related guidance signals during credit assignments in CTDE. To address this, we propose Intrinsic Action Tendency Consistency, a novel approach for cooperative multi-agent reinforcement learning. It integrates intrinsic rewards, obtained through an action model, into a reward-additive CTDE (RA-CTDE) framework. We formulate an action model that enables surrounding agents to predict the central agent's action tendency. Leveraging these predictions, we compute a cooperative intrinsic reward that encourages agents to match their actions with their neighbors' predictions. We establish the equivalence between RA-CTDE and CTDE through theoretical analyses, demonstrating that CTDE's training process can be achieved using agents' individual targets. Building on this insight, we introduce a novel method to combine intrinsic rewards and CTDE. Extensive experiments on challenging tasks in SMAC and GRF benchmarks showcase the improved performance of our method.

This paper presents multi-agent reinforcement learning frameworks for the low-level control of a quadrotor UAV. While single-agent reinforcement learning has been successfully applied to quadrotors, training a single monolithic network is often data-intensive and time-consuming. To address this, we decompose the quadrotor dynamics into the translational dynamics and the yawing dynamics, and assign a reinforcement learning agent to each part for efficient training and performance improvements. The proposed multi-agent framework for quadrotor low-level control that leverages the underlying structures of the quadrotor dynamics is a unique contribution. Further, we introduce regularization terms to mitigate steady-state errors and to avoid aggressive control inputs. Through benchmark studies with sim-to-sim transfer, it is illustrated that the proposed multi-agent reinforcement learning substantially improves the convergence rate of the training and the stability of the controlled dynamics.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Many systems like autonomous vehicle fleets and drone swarms can be modeled as Multi-Agent Reinforcement Learning (MARL) tasks, which deal with how multiple machines can learn to collaborate, coordinate, compete, and collectively learn. It's been shown that machine learning algorithms -- particularly reinforcement learning algorithms -- are well-suited to MARL tasks. But it's often challenging to efficiently scale them up to hundreds or even thousands of machines. One solution is a technique called centralized training and decentralized execution (CTDE), which allows an algorithm to train using data from multiple machines but make predictions for each machine individually (e.g., like when a driverless car should turn left).