Reinforcement learning (RL) has shown superior performance in solving sequential
decision problems. In recent years, RL is gradually being used to solve unmanned driving …

With the development of UAV technology, the task allocation problem of multiple UAVs is
remarkable, but most of these existing heuristic methods are easy to fall into the problem of …

Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …

Objectives To systematically quantify the diagnostic accuracy and identify potential
covariates affecting the performance of artificial intelligence (AI) in diagnosing orthopedic …

Large-scale multiagent reinforcement learning requires huge computation and space costs,
and the too-long execution process makes it hard to train policies for agents. This work …

Aiming at addressing the problem of manoeuvring decision‐making in UAV air combat, this
study establishes a one‐to‐one air combat model, defines missile attack areas, and uses the …

The demand for autonomous motion control of unmanned aerial vehicles in air combat is
boosted as taking the initiative in combat appears more and more crucial. Unmanned aerial …

Multiagent reinforcement learning (RL) training is usually difficult and time-consuming due to
mutual interference among agents. Safety concerns make an already difficult training …

Recent developments in reinforcement learning (RL) have been able to derive optimal
policies for sophisticated and capable agents, and shown to achieve human-level …

Recently, Moore-Penrose inverse (MPI)-based parameter fine-tuning of fully connected (FC)
layers in pretrained deep convolutional neural networks (DCNNs) has emerged within the …