Autonomous agents and multi-agent systemsCoordinating multiple agents via reinforcement learning. Chen G,Yang Zh. Autonomous Agents and Multi-Agent Systems(S1387-2532) . 2005Gang Chen,Zhonghua Yang,Hao He,Kiah Mok Goh.Coordinating Multiple Agents via Reinforcement Learning[J]. Autonomous Agents and ...
第一个GPT代理的损失函数设计如下: L1(x;Θ1)=[logP(x)Prior−logP(x)Agent1+σ1⋅s(x)]2 其中Θ1是第一个代理的参数,x是生成的分子,σ1是控制分数项的系数,P(x)model指的是模型生成x的可能性。应该注意的是,通常P(x)_{\text{Prior}} < P(x)_{\text{Agent}}。 此外,我们鼓励...
Last but not least, we believe that the feedback recovery mechanism and the two-stage action selection mechanism can also be used in general distributed multi-agent reinforcement learning problems in which feedback information on rewards can be corrupted. 展开 ...
Whittle index is a heuristic tool that leads to good performance for the restless bandits problem. In this paper, we extend Whittle index to a new multi-agent reinforcement learning (MARL) setting with multiple discrete actions and a possibly changing constraint ...
This paper proposes a new detection approach for multiple landmarks based on multi-agent reinforcement learning. Our hypothesis is that the position of all anatomical landmarks is interdependent and non-random within the human anatomy, thus finding one landmark can help to deduce the location of...
Multi-Agent Reinforcement Learning in NOMA-aided UAV Networks for Cellular Offloading A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs), while the non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve ...
For this example you create two reinforcement learning agents. Both agents operate at the same sample time in this example. Set the sample time value (in seconds). Ts = 0.1; When you create the agent, the initial parameters of the critic network are initialized with random values. Fix the...
Qiu, D., Wang, Y., Zhang, T., Sun, M. & Strbac, G. Hierarchical multi-agent reinforcement learning for repair crews dispatch control towards multi-energy microgrid resilience., 120826.(2023). Liu, H., Li, J. & Ge, S. Research on hierarchical control and optimization learning method...
6) multi agent reinforcement learning 多智能体增强学习补充资料:增强体 分子式:CAS号:性质:为复合材料中承受载荷的组分。按几何形状来分,增强体有零维的颗粒状、一维的纤维状、二维的片状和三维的立体结构。按属性来分则有无机和有机增强体,其中有合成的也有天然的。主要的增强体是纤维状的,如无机的玻璃纤维...
This paper introduces MGAN for collaborative multi-agent reinforcement learning, a new algorithm that combines graph convolutional networks and value-decomposition methods. MGAN learns the representation of agents from different perspectives through multiple graph networks, and realizes the proper allocation ...