Human-robot teaming: grand challenges
Abstract Purpose of Review Current real-world interaction between humans and robots is
extremely limited. We present challenges that, if addressed, will enable humans and robots …
extremely limited. We present challenges that, if addressed, will enable humans and robots …
Curriculum learning for reinforcement learning domains: A framework and survey
Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks
in which the agent has only limited environmental feedback. Despite many advances over …
in which the agent has only limited environmental feedback. Despite many advances over …
Transformers in reinforcement learning: a survey
P Agarwal, AA Rahman, PL St-Charles… - arXiv preprint arXiv …, 2023 - arxiv.org
Transformers have significantly impacted domains like natural language processing,
computer vision, and robotics, where they improve performance compared to other neural …
computer vision, and robotics, where they improve performance compared to other neural …
Updet: Universal multi-agent reinforcement learning via policy decoupling with transformers
Recent advances in multi-agent reinforcement learning have been largely limited in training
one model from scratch for every new task. The limitation is due to the restricted model …
one model from scratch for every new task. The limitation is due to the restricted model …
Collision-avoiding flocking with multiple fixed-wing UAVs in obstacle-cluttered environments: a task-specific curriculum-based MADRL approach
Multiple unmanned aerial vehicles (UAVs) are able to efficiently accomplish a variety of
tasks in complex scenarios. However, developing a collision-avoiding flocking policy for …
tasks in complex scenarios. However, developing a collision-avoiding flocking policy for …
Asynchronous multi-agent reinforcement learning for efficient real-time multi-robot cooperative exploration
We consider the problem of cooperative exploration where multiple robots need to
cooperatively explore an unknown region as fast as possible. Multi-agent reinforcement …
cooperatively explore an unknown region as fast as possible. Multi-agent reinforcement …
PASCAL: population-specific curriculum-based MADRL for collision-free flocking with large-scale fixed-wing UAV swarms
Flocking with a swarm of unmanned aerial vehicles (UAVs) has been playing an important
role in various applications. However, the complexity of developing a collision-free flocking …
role in various applications. However, the complexity of developing a collision-free flocking …
A deep reinforcement learning-based method applied for solving multi-agent defense and attack problems
L Huang, M Fu, H Qu, S Wang, S Hu - Expert Systems with Applications, 2021 - Elsevier
Learning to cooperate among agents has always been an important research topic in
artificial intelligence. Multi-agent defense and attack, one of the important issues in multi …
artificial intelligence. Multi-agent defense and attack, one of the important issues in multi …
Coach-player multi-agent reinforcement learning for dynamic team composition
In real-world multi-agent systems, agents with different capabilities may join or leave without
altering the team's overarching goals. Coordinating teams with such dynamic composition is …
altering the team's overarching goals. Coordinating teams with such dynamic composition is …
Randomized entity-wise factorization for multi-agent reinforcement learning
Multi-agent settings in the real world often involve tasks with varying types and quantities of
agents and non-agent entities; however, common patterns of behavior often emerge among …
agents and non-agent entities; however, common patterns of behavior often emerge among …