Deep reinforcement learning has produced many success stories in recent years. Some
example fields in which these successes have taken place include mathematics, games …

Numerous path-planning studies have been conducted in past decades due to the
challenges of obtaining optimal solutions. This paper reviews multi-robot path-planning …

Mobile robots play crucial roles in industry and commerce, and automatic guided vehicles
(AGV) are one of the primary parts of smart manufactory and intelligent logistics. Path …

Abstract Model-agnostic meta-learning (MAML) is one of the most successful meta-learning
algorithms. It has a bi-level optimization structure where the outer-loop process learns a …

The use of reinforcement learning (RL) for dynamic obstacle avoidance (DOA) algorithms
and path planning (PP) has become increasingly popular in recent years. Despite the …

This study proposes a multi-strategy evolutionary artificial ecosystem optimizer based on
reinforcement learning (MEAEO-RL) to tackle the collaborative path-planning problem of …

A common assumption of coverage path planning research is a static environment. Such
environments require only a single visit to each area to achieve coverage. However, some …

Deep reinforcement learning-based (DRL-based) path planning in the unknown
environment is studied under continuous action space. We extend the TD3 (twin-delayed …

Multi-agent pathfinding (MAPF) is a critical field in many large-scale robotic applications,
often being the fundamental step in multi-agent systems. The increasing complexity of MAPF …

Defining inter-well connectivity is very important for the water injection development of
carbonate fractured-vuggy reservoirs. However, most conventional methods based on …