IEEE

Deep Reinforcement Learning-Based Motion Planning and PDE Control for Flexible Manipulators

Abstract: This article presents a motion planning and control framework for flexible robotic manipulators, integrating deep reinforcement learning (DRL) with a nonlinear partial differential equation ...
IEEE

Reinforcement Learning for Multi-Agent Path Finding in Large-Scale Warehouses via Distributed Policy Evolution

Abstract: Efficient multi-agent path finding (MAPF) is essential for large-scale warehousing and logistics systems. Despite the potential of reinforcement learning (RL) methods, current approaches ...
Frontiers

Solving robotics tasks with prior demonstration via exploration-efficient deep reinforcement learning

This paper proposes an exploration-efficient deep reinforcement learning with reference (DRLR) policy framework for learning robotics tasks incorporating demonstrations. The DRLR framework is ...
Frontiers

LG-H-PPO: offline hierarchical PPO for robot path planning on a latent graph

The path planning capability of autonomous robots in complex environments is crucial for their widespread application in the real world. However, long-term decision-making and sparse reward signals ...