In recent years, deep learning-based approaches for visual-inertial odometry (VIO) have shown remarkable performance outperforming traditional geometric methods.
In this work, we are dedicated to multi-target active object tracking (AOT), where there are multiple targets as well as multiple cameras in the environment.
To balance the training complexity and the diversity of agents' behaviors, we propose a novel framework for learning dynamic subtask assignment (LDSA) in cooperative MARL.
In this way, CTDS balances the full utilization of global observation during training and the feasibility of decentralized execution for online inference.
To the best of our knowledge, this work is the first to study the unexpected crashes in the multi-agent system.
In our approach, we regard each camera as an agent and address AMOT with a multi-agent reinforcement learning solution.
During the interactions, the uncertainty of environment and reward will inevitably induce stochasticity in the long-term returns and the randomness can be exacerbated with the increasing number of agents.
Recent works reveal that feature or label smoothing lies at the core of Graph Neural Networks (GNNs).
To the best of our knowledge, this is the first deep JSCC scheme that can automatically adjust its rate using a single network model.
First, GNNs can learn higher-order structural information by stacking more layers but can not deal with large depth due to the over-smoothing issue.
We present a deep learning based joint source channel coding (JSCC) scheme for wireless image transmission over multipath fading channels with non-linear signal clipping.
Details of Monarch butterfly migration from the U. S. to Mexico remain a mystery due to lack of a proper localization technology to accurately localize and track butterfly migration.