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 …
Deep reinforcement learning: An overview
Y Li - arXiv preprint arXiv:1701.07274, 2017 - arxiv.org
We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …
We discuss six core elements, six important mechanisms, and twelve applications. We start …
Multi-agent deep reinforcement learning: a survey
S Gronauer, K Diepold - Artificial Intelligence Review, 2022 - Springer
The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …
Although the multi-agent domain has been overshadowed by its single-agent counterpart …
Deep reinforcement learning
SE Li - Reinforcement learning for sequential decision and …, 2023 - Springer
Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …
decision strategies. However, in many cases, it is desirable to learn directly from …
Multi-agent reinforcement learning: An overview
Multi-agent systems can be used to address problems in a variety of domains, including
robotics, distributed control, telecommunications, and economics. The complexity of many …
robotics, distributed control, telecommunications, and economics. The complexity of many …
A comprehensive survey of multiagent reinforcement learning
Multiagent systems are rapidly finding applications in a variety of domains, including
robotics, distributed control, telecommunications, and economics. The complexity of many …
robotics, distributed control, telecommunications, and economics. The complexity of many …
A benchmark for the comparison of 3-d motion segmentation algorithms
Over the past few years, several methods for segmenting a scene containing multiple rigidly
moving objects have been proposed. However, most existing methods have been tested on …
moving objects have been proposed. However, most existing methods have been tested on …
Episodic exploration for deep deterministic policies: An application to starcraft micromanagement tasks
We consider scenarios from the real-time strategy game StarCraft as new benchmarks for
reinforcement learning algorithms. We propose micromanagement tasks, which present the …
reinforcement learning algorithms. We propose micromanagement tasks, which present the …
Hierarchical cooperative multi-agent reinforcement learning with skill discovery
Human players in professional team sports achieve high level coordination by dynamically
choosing complementary skills and executing primitive actions to perform these skills. As a …
choosing complementary skills and executing primitive actions to perform these skills. As a …
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 …