路径规划的目的是让机器人在移动过程中既能避开障碍物, 又能快速规划出最短路径.
在分析基于强化学习的路径规划算法优缺点的基础上, 引出能够在复杂动态环境下进行良好路径 …

While auxiliary tasks play a key role in shaping the representations learnt by reinforcement
learning agents, much is still unknown about the mechanisms through which this is …

The first comprehensive guide to distributional reinforcement learning, providing a new
mathematical formalism for thinking about decisions from a probabilistic perspective …

Stock value prediction and trading, a captivating and complex research domain, continues to
draw heightened attention. Ensuring profitable returns in stock market investments demands …

It is an essential capability of indoor mobile robots to avoid various kinds of obstacles.
Recently, multimodal deep reinforcement learning (DRL) methods have demonstrated great …

Explaining the behavior of AI systems is an important problem that, in practice, is generally
avoided. While the XAI community has been developing an abundance of techniques, most …

Intelligent game agents are crafted using AI technologies to mimic player behavior and
make decisions autonomously. Over the past decades, the scope of intelligent agents has …

We study the denial-of-service (DoS) attack power allocation optimization in a multiprocess
cyber–physical system (CPS), where sensors observe different dynamic processes and …

Policy gradient algorithms have proven to be successful in diverse decision making and
control tasks. However, these methods suffer from high sample complexity and instability …

Visual-audio navigation (VAN) is attracting more and more attention from the robotic
community due to its broad applications, eg, household robots and rescue robots. In this …