Active simultaneous localization and mapping (SLAM) is the problem of planning and
controlling the motion of a robot to build the most accurate and complete model of the …

In this review, we present an analysis of the most used multi-agent reinforcement learning
algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the …

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 …

: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 …

With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

In the last few years, deep multi-agent reinforcement learning (RL) has become a highly
active area of research. A particularly challenging class of problems in this area is partially …

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep
learning. This field of research has been able to solve a wide range of complex …