Multi-agent reinforcement learning (MARL) is of importance for variable real-world applications but remains more challenges like stationarity and scalability. While recently value function factorization methods have obtained empirical good results in cooperative multi-agent environment, these works mostly focus on the decomposable learning structures. Inspired by the application of attention mechanism in machine translation and other related domains, we propose an attention based approach called attention value decomposition network (AVD-Net), which capitalizes on the coordination relations between agents. AVD-Net employs centralized training with decentralized execution (CTDE) paradigm, which factorizes the joint action-value functions with only local observations and actions of agents. Our method is evaluated on multi-agent particle environment (MPE) and StarCraft micromanagement environment (SMAC). The experiment results show the strength of our approach compared to existing methods with state-of-the-art performance in cooperative scenarios.

Current 3D object detection methods adopt an analogous box prediction structure with the 2D methods, which predict center and size of the object simultaneously in a box regression procedure, leading to the poor performance of 3D detector to a great extent. In this work, we propose S-VoteNet, which converts the prediction of 3D bounding box into two parts: center prediction and size prediction. By introducing a novel spherical proposal, S-VoteNet uses vote groups to predict the center and radius of object rather than all parameters of 3D bounding box. The prediction of radius is used to constrain the object size, and the radius-based spherical center loss is applied to measure the geometric distance between the proposal and ground-truth. To make better use of the geometric information provided by point cloud, S-VoteNet gathers seed points whose corresponding votes are within the vote groups for seed group generation. Seed groups are then consumed for box size regression and orientation estimation. By decoupling the localization and size estimation, our method effectively reduces the regression pressure of the 3D detector. Experimental results on SUN RGB-D 3D detection benchmark demonstrate that our S-VoteNet achieves state-of-the-art performance by using only point cloud as input.