A survey on intrinsic motivation in reinforcement learning
The reinforcement learning (RL) research area is very active, with an important number of
new contributions; especially considering the emergent field of deep RL (DRL). However a …
new contributions; especially considering the emergent field of deep RL (DRL). However a …
Trajectory diversity for zero-shot coordination
We study the problem of zero-shot coordination (ZSC), where agents must independently
produce strategies for a collaborative game that are compatible with novel partners not seen …
produce strategies for a collaborative game that are compatible with novel partners not seen …
Too many cooks: Bayesian inference for coordinating multi‐agent collaboration
Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating
to solve a single task together and other times dividing it up into sub‐tasks to work on in …
to solve a single task together and other times dividing it up into sub‐tasks to work on in …
Deep learning applications in games: a survey from a data perspective
This paper presents a comprehensive review of deep learning applications in the video
game industry, focusing on how these techniques can be utilized in game development …
game industry, focusing on how these techniques can be utilized in game development …
Hierarchical reinforcement learning with adaptive scheduling for robot control
Z Huang, Q Liu, F Zhu - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Conventional hierarchical reinforcement learning (HRL) relies on discrete options to
represent explicitly distinguishable knowledge, which may lead to severe performance …
represent explicitly distinguishable knowledge, which may lead to severe performance …
Towards efficient long-horizon decision-making using automated structure search method of hierarchical reinforcement learning for edge artificial intelligence
Hierarchical reinforcement learning (HRL) is a promising approach for efficiently solving
various long-horizon decision-making tasks in the Internet of Things (IoT) domain. However …
various long-horizon decision-making tasks in the Internet of Things (IoT) domain. However …
A study of deep reinforcement learning based recommender systems
With the advancement in new technologies and a massive amount of data every day,
various tools and models are being introduced to tackle such data. Such a branch of …
various tools and models are being introduced to tackle such data. Such a branch of …
VNE-HRL: A proactive virtual network embedding algorithm based on hierarchical reinforcement learning
J Cheng, Y Wu, Y Lin, E Yuepeng… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Virtual network embedding (VNE) that instantiates virtualized networks on a substrate
infrastructure, is one of the key research problems for network virtualization. Most existing …
infrastructure, is one of the key research problems for network virtualization. Most existing …
Beyond rewards: a hierarchical perspective on offline multiagent behavioral analysis
Each year, expert-level performance is attained in increasingly-complex multiagent
domains, where notable examples include Go, Poker, and StarCraft II. This rapid …
domains, where notable examples include Go, Poker, and StarCraft II. This rapid …
Resonating minds—emergent collaboration through hierarchical active inference
Working together on complex collaborative tasks requires agents to coordinate their actions.
Doing this explicitly or completely prior to the actual interaction is not always possible nor …
Doing this explicitly or completely prior to the actual interaction is not always possible nor …