.png){kind=icon}
返回
Dynamic distributed constraint optimization using multi-agent reinforcement learning
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-022-06820-7.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
An inherent difficulty in dynamic distributed constraint optimization problems (dynamic DCOP) is the uncertainty of future events when making an assignment at the current time. This dependency is not well addressed in the research community. This paper proposes a reinforcement-learning-based solver for dynamic distributed constraint optimization. We show that reinforcement learning techniques are an alternative approach to solve the given problem over time and are computationally more efficient than sequential DCOP solvers. We also use the novel heuristic to obtain the correct results and describe a formalism that has been adopted to model dynamic DCOPs with cooperative agents. We evaluate this approach in dynamic weapon target assignment (dynamic WTA) problem, via experimental results. We observe that the system dynamic WTA problem remains a safe zone after convergence while satisfying the constraints. Moreover, in the experiment we have implemented the agents that finally converge to the correct assignment.
Keyword:
Dynamic distributed constraint optimization problem
Reinforcement learning
Multi-agent systems
Weapon target assignment
Markov decision process
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
机构
引用论文
分享收藏Roles of thrombin and platelet membrane glycoprotein IIb/IIIa in platelet-subendothelial deposition after angioplasty in an ex vivo whole artery model..png)
Circulation
OAAI
CirculationIF0
OAAI分享收藏Severe Acute Respiratory Distress Syndrome in an Adult Patient With Human Metapneumovirus Infection Successfully Managed With Veno-Venous Extracorporeal Membrane Oxygenation成人人类副流感病毒感染患者的重症急性呼吸窘迫综合征，经静脉-静脉体外膜氧合成功管理
PREAI
分享收藏
查看更多内容