Pinned Repositories
CSC_466-579_Project_Machine_Learning
Co-tier and Cross-tier Uplink Interference Mitigation using Q-learning
Multiple-Agent-Interaction
Comparison of four Q-learning algorithms that include interaction of agents.
Optimization_Algorithm
梯度下降、牛顿法、共轭梯度法、蒙特卡洛、模拟退火、粒子群、蚁群、全局最优搭配局部最优算法的matlab和python程序
proximity
Power allocation in a dense cellular network using Q-learning
proximity-1
A Machine Learning Approach for Power Allocation in HetNets Considering QoS
Q-Learning-Based-Power-Control-Algorithm-for-D2D-Communication
D2D communication as a multi-agents system, and power control is achieved by maximizing system capacity while maintaining the requirement of quality of service(QoS) from cellular users.
reinforcement-learning
Implementation of Single-Agent and Multi-Agent Reinforcement Learning Algorithms. MATLAB.
bingruo's Repositories
bingruo/CSC_466-579_Project_Machine_Learning
Co-tier and Cross-tier Uplink Interference Mitigation using Q-learning
bingruo/Multiple-Agent-Interaction
Comparison of four Q-learning algorithms that include interaction of agents.
bingruo/Optimization_Algorithm
梯度下降、牛顿法、共轭梯度法、蒙特卡洛、模拟退火、粒子群、蚁群、全局最优搭配局部最优算法的matlab和python程序
bingruo/proximity
Power allocation in a dense cellular network using Q-learning
bingruo/proximity-1
A Machine Learning Approach for Power Allocation in HetNets Considering QoS
bingruo/Q-Learning-Based-Power-Control-Algorithm-for-D2D-Communication
D2D communication as a multi-agents system, and power control is achieved by maximizing system capacity while maintaining the requirement of quality of service(QoS) from cellular users.
bingruo/reinforcement-learning
Implementation of Single-Agent and Multi-Agent Reinforcement Learning Algorithms. MATLAB.