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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 …
Self-organized group for cooperative multi-agent reinforcement learning
Centralized training with decentralized execution (CTDE) has achieved great success in
cooperative multi-agent reinforcement learning (MARL) in practical applications. However …
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 …
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 …
area blackouts, which highlights the importance of rapidly and efficiently restoring the …