A survey on offline reinforcement learning: Taxonomy, review, and open problems
RF Prudencio, MROA Maximo… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the widespread adoption of deep learning, reinforcement learning (RL) has
experienced a dramatic increase in popularity, scaling to previously intractable problems …
experienced a dramatic increase in popularity, scaling to previously intractable problems …
User-centric cell-free massive MIMO networks: A survey of opportunities, challenges and solutions
Densification of network base stations is indispensable to achieve the stringent Quality of
Service (QoS) requirements of future mobile networks. However, with a dense deployment of …
Service (QoS) requirements of future mobile networks. However, with a dense deployment of …
Reaching the limit in autonomous racing: Optimal control versus reinforcement learning
A central question in robotics is how to design a control system for an agile mobile robot.
This paper studies this question systematically, focusing on a challenging setting …
This paper studies this question systematically, focusing on a challenging setting …
Multi-agent deep reinforcement learning: a survey
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 …
Autonomous experimentation systems for materials development: A community perspective
Solutions to many of the world's problems depend upon materials research and
development. However, advanced materials can take decades to discover and decades …
development. However, advanced materials can take decades to discover and decades …
Multi-agent reinforcement learning: A selective overview of theories and algorithms
Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …
has registered tremendous success in solving various sequential decision-making problems …
[图书][B] Algorithms for decision making
A broad introduction to algorithms for decision making under uncertainty, introducing the
underlying mathematical problem formulations and the algorithms for solving them …
underlying mathematical problem formulations and the algorithms for solving them …
Offline reinforcement learning with realizability and single-policy concentrability
Sample-efficiency guarantees for offline reinforcement learning (RL) often rely on strong
assumptions on both the function classes (eg, Bellman-completeness) and the data …
assumptions on both the function classes (eg, Bellman-completeness) and the data …
Learning-based model predictive control: Toward safe learning in control
Recent successes in the field of machine learning, as well as the availability of increased
sensing and computational capabilities in modern control systems, have led to a growing …
sensing and computational capabilities in modern control systems, have led to a growing …
Multi-agent deep reinforcement learning-based trajectory planning for multi-UAV assisted mobile edge computing
An unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) framework is
proposed, where several UAVs having different trajectories fly over the target area and …
proposed, where several UAVs having different trajectories fly over the target area and …