Abstract Safe Reinforcement Learning can be defined as the process of learning policies
that maximize the expectation of the return in problems in which it is important to ensure …

With outstanding features, machine learning (ML) has become the backbone of numerous
applications in wireless networks. However, the conventional ML approaches face many …

:强化学习是学习环境状态到动作的一种映射,并且能够获得最大的奖赏信号.在大规模状 Page 1
第40 卷 计算机学报 Vol. 40 2017 年论文在线出版号No.1 CHINESE JOURNAL OF …

Reinforcement learning is a learning paradigm for solving sequential decision-making
problems. Recent years have witnessed remarkable progress in reinforcement learning …

Many real-world tasks involve multiple agents with partial observability and limited
communication. Learning is challenging in these settings due to local viewpoints of agents …

Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with
other agents through autonomous exploration of the environment. However, learning a …

The reinforcement learning (RL) community has made great strides in designing algorithms
capable of exceeding human performance on specific tasks. These algorithms are mostly …

Learning tabula rasa, that is without any prior knowledge, is the prevalent workflow in
reinforcement learning (RL) research. However, RL systems, when applied to large-scale …

Transfer learning is the improvement of learning in a new task through the transfer of
knowledge from a related task that has already been learned. While most machine learning …

The reinforcement learning paradigm is a popular way to address problems that have only
limited environmental feedback, rather than correctly labeled examples, as is common in …