深度Q学习改善自适应自组织网络案例 # Deep-Q-Learning-SON-Perf-Improvement The requirement for running this code is to obtain license and access of the Vienna LTE-A simulator, found at: https://www.nt.tuwien.ac.at/research/mobile-communications/vccs/vienna-lte-a-simu
深度Q-Learning与model-based方法结合来解决连续动作问题
Model-free reinforcement learning has been successfully applied to a range of challenging problems, and has recently been extended to handle large neural network policies and value functions. However, the sample
Deep Reinforcement Learning Hands-On
by Maxim LapanTable of contents
Deep reinforcement Learning Hands-On
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The basic theory of reinforcement and the idea of distributed Q-Learning are introduced in this paper. Based on the analysis of distributed Q-Learning in the urban traffic coordination control, reward function and weight function are presented. The e
Q-learning is a reinforcement learning method to solve Markovian decision problems with incomplete information. The design of reward function is an important factor that affects the learning results of Q-learning. A method to design the reward functi
