Hierarchical reinforcement learning: A comprehensive survey
Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of
challenging long-horizon decision-making tasks into simpler subtasks. During the past …
challenging long-horizon decision-making tasks into simpler subtasks. During the past …
Contrasting centralized and decentralized critics in multi-agent reinforcement learning
Centralized Training for Decentralized Execution, where agents are trained offline using
centralized information but execute in a decentralized manner online, has gained popularity …
centralized information but execute in a decentralized manner online, has gained popularity …
Asynchronous actor-critic for multi-agent reinforcement learning
Synchronizing decisions across multiple agents in realistic settings is problematic since it
requires agents to wait for other agents to terminate and communicate about termination …
requires agents to wait for other agents to terminate and communicate about termination …
Scalable multi-agent covering option discovery based on kronecker graphs
Covering option discovery has been developed to improve the exploration of RL in single-
agent scenarios with sparse reward signals, through connecting the most distant states in …
agent scenarios with sparse reward signals, through connecting the most distant states in …
Solving multi-agent routing problems using deep attention mechanisms
Routing delivery vehicles to serve customers in dynamic and uncertain environments like
dense city centers is a challenging task that requires robustness and flexibility. Most existing …
dense city centers is a challenging task that requires robustness and flexibility. Most existing …
On centralized critics in multi-agent reinforcement learning
Abstract Centralized Training for Decentralized Execution, where agents are trained offline
in a centralized fashion and execute online in a decentralized manner, has become a …
in a centralized fashion and execute online in a decentralized manner, has become a …
Optimizing delegation in collaborative human-ai hybrid teams
When humans and autonomous systems operate together as what we refer to as a hybrid
team, we of course wish to ensure the team operates successfully and effectively. We refer to …
team, we of course wish to ensure the team operates successfully and effectively. We refer to …
Optimizing delegation between human and ai collaborative agents
In the context of humans operating with artificial or autonomous agents in a hybrid team, it is
essential to accurately identify when to authorize those team members to perform actions …
essential to accurately identify when to authorize those team members to perform actions …
An efficient transfer learning framework for multiagent reinforcement learning
Transfer Learning has shown great potential to enhance single-agent Reinforcement
Learning (RL) efficiency. Similarly, Multiagent RL (MARL) can also be accelerated if agents …
Learning (RL) efficiency. Similarly, Multiagent RL (MARL) can also be accelerated if agents …
[PDF][PDF] Multi-agent covering option discovery based on kronecker product of factor graphs
Covering skill (aka, option) discovery has been developed to improve the exploration of
reinforcement learning in single-agent scenarios, where only sparse reward signals are …
reinforcement learning in single-agent scenarios, where only sparse reward signals are …