.png){kind=icon}
返回
Q-Managed: A new algorithm for a multiobjective reinforcement learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2020.114228.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multi-objective reinforcement learning (MORL) involves the use of reinforcement learning techniques to address problems with multiple objectives, conflicting or not. Among the main techniques used to treat this class of problems, we saw that they are limited by some factors, such as the Pareto Front shape and computational cost. This paper proposes a new iterative algorithm based on the single-policy approach, called Q-Managed. We use a hybrid multi-objective optimization (MOO) method that provides the mathematical guarantee that all policies belonging to the Pareto Front can be found, regardless of whether it is concave, convex or a mixture of both. Another important aspect that is worth mentioning is that its simplicity and performance are from a single-policy algorithms. To validate our proposal, we use the traditional MORL benchmarks and with different configurations of the Pareto Front. The Q-Managed shows success in finding all the optimal policies in all environments, surpassing all the single-policy algorithms in the literature in terms of policy quality. Based on the used benchmarks, its effectiveness can also be equated to the best multi-policy algorithms. The hypervolume metric was used to compare the quality of the policies found by our algorithm with those found in the state of the art. Extensions for non-episodic environments and stochastic transition functions are also introduced.
Keyword:
Multiobjective reinforcement learning
epsilon-Constraint
Q-Learning
Pareto dominance
Single-policy approach
Hypervolume
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
2.9W
被引数:
10.2W
机构
引用论文
An interactive fuzzy satisficing method for multiobjective 0-1 programming problems with fuzzy numbers through genetic algorithms with double strings通过双字符串遗传算法求解具有模糊数的多目标0-1规划问题的交互式模糊满足方法
PREAI
分享收藏Printable cellulose-based electroconductive composites for sensing elements in paper electronics
OAAI
OAAI分享收藏Deficient cerebellar long-term depression and impaired motor learning in mGluR1 mutant mice.png)
Cell
CellIF0
PREAI
分享收藏
Turning a Forgotten Geological Heritage into a Geological Park: Developing Stonehammer GeoparkGeoheritage
GeoheritageIF0
PREAI
分享收藏
查看更多内容