Joint design of routing selection and user association in multi-hop mmWave IABN: a multi-agent and double DQN framework: Joint design of routing selection and user association...MmWave IABNUser associationRouting selectionDRLMulti-agent double deep Q-network...
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Two independent DQN agents are trained. One agent selects operation sequences, while the other assigns jobs to machines. Du et al. (2021) [6] considered an FJSP with time-of-use electricity price constraint and dual-objective optimization for the makespan and total price and proposed a ...
DQN Deep Q-learning Network FFANN Feed-Forward Artificial Neural Network MARL Multi-Agent Reinforcement Learning MIMO Multiple-input multiple-output ML Machine learning OLSR Optimized Link State Routing References Perera, S.M.; Myers, R.J.; Sullivan, K.; Byassee, K.; Song, H.; Madanayake, ...
The main contribution of this study is the development of a framework for centralized multi-agent planning problems that outperform Multi-Agent DQNs for solving large-scale MMDPs and analysis of their performance. The second contribution of the study is to apply the framework to the problem of ...
For instance, in [30], the authors proposed A-DQN to determine the task allocation subject to time constraints. However, they aimed to minimize the total reward given to the users selected to perform the sensing task. Therefore, by considering the task allocation subject to time constraints of...
The main contribution of this study is the development of a framework for centralized multi-agent planning problems that outperform Multi-Agent DQNs for solving large-scale MMDPs and analysis of their performance. The second contribution of the study is to apply the framework to the problem of ...
The environment calculates the reward based on the designed reward function, sends complete {s, a, r, s’} to the agent, and records it in the experience replay memory. In the proposed algorithm, E-DQN uses the prioritized experience replay method to choose valuable data for training, and ...
Through Deep Q-learning Network (DQN) Adjustment and Error Priority Learning (EPL), the IDS could change the tradeoff or undergo retraining based on detection performance and driving conditions. The works discussed were performed in a simulated environment using tools like NS2, SUMO, and OpenStreet...
Multi-robot path planning based on a deep reinforcement learning DQN algorithm. CAAI TRIT. 2020, 5, 177–183. [Google Scholar] [CrossRef] Koval, A.; Mansouri, S.S.; Nikolakopoulos, G. Multi-Agent Collaborative Path Planning Based on Staying Alive Policy. Robotics 2020, 9, 101. [...