Unmanned surface vessel (USV) operations will change the future form of maritime wars
profoundly, and one of the critical factors for victory is the cluster intelligence of USVs …

Abstract In Off-Policy reinforcement learning (RL), the experience imbalance problem can
affect learning performance. The experience imbalance problem refers to the phenomenon …

This study aims to solve the problem of sparse reward and local convergence when using a
reinforcement learning algorithm as the controller of an AUV. Based on the generative …

The fundamental concept behind option-based hierarchical reinforcement learning (O-HRL)
is to obtain temporal coarse-grained actions and abstract complex situations. Although O …

Due to the complexity and uncertainty of the traffic, planning for autonomous driving (AD) on
highway is challenging. Traditional planning algorithms have the problems of low and …

Multigoal reinforcement learning (RL) extends the typical RL with goal-conditional value
functions and policies. One efficient multigoal RL algorithm is the hindsight experience …

In order to improve the ability of spacecraft formation to evade multiple interceptors, aiming
at the low success rate of traditional procedural maneuver evasion, a multi-agent …

In order to solve the problem of unstructured environment and complex operation task of
space robot, this paper use DDPG algorithm which is data-driven and model free in the …

**年来, **化学**在游戏, 机器人控制等序列决策领域都获得了巨大的成功, 但是大量实际问题中
奖励信号十分稀疏, 导致智能体难以从与环境的交互中学**到最优的策略, 这一问题被称为稀疏 …

The unique ram air aerodynamic shape and control rope pulling course of the parafoil
system make it difficult to realize its precise control. At present, the commonly used control …