Human-robot teaming: grand challenges

M Natarajan, E Seraj, B Altundas, R Paleja, S Ye… - Current Robotics …, 2023 - Springer
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 …

Curriculum learning for reinforcement learning domains: A framework and survey

S Narvekar, B Peng, M Leonetti, J Sinapov… - Journal of Machine …, 2020 - jmlr.org
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 …

Self-organized group for cooperative multi-agent reinforcement learning

J Shao, Z Lou, H Zhang, Y Jiang… - Advances in Neural …, 2022 - proceedings.neurips.cc
Centralized training with decentralized execution (CTDE) has achieved great success in
cooperative multi-agent reinforcement learning (MARL) in practical applications. However …

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 …

A multi-agent reinforcement learning method for distribution system restoration considering dynamic network reconfiguration

R Si, S Chen, J Zhang, J Xu, L Zhang - Applied Energy, 2024 - Elsevier
Extreme weather, chain failures, and other events have increased the probability of wide-
area blackouts, which highlights the importance of rapidly and efficiently restoring the …