Deep Reinforcement Learning with Symmetric Prior for Predictive Power Allocation to Mobile Users
Deep reinforcement learning has been applied for a variety of wireless tasks, which is however known with high training and inference complexity. In this paper, we resort to deep deterministic policy gradient (DDPG) algorithm to optimize predictive power allocation among K mobile users requesting video streaming, which minimizes the energy consumption of the network under the no-stalling constraint of each user. To reduce the sampling complexity and model size of the DDPG, we exploit a kind of symmetric prior inherent in the actor and critic networks: permutation invariant and equivariant properties, to design the neural networks. Our analysis shows that the free model parameters of the DDPG can be compressed by 2/K^2. Simulation results demonstrate that the episodes required by the learning model with the symmetric prior to achieve the same performance as the vanilla policy reduces by about one third when K = 10.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep Reinforcement LearningReinforcement Learning (RL)Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
The Spoils of Algorithmic Collusion: Profit Allocation Among Asymmetric Firms
We study the propensity of independent algorithms to collude in repeated Cournot duopoly games. Specifically, we investigate the predictive power of different oligopoly and bargaining solutions regarding the effect of as…
Improving Learning Efficiency for Wireless Resource Allocation with Symmetric Prior
Improving learning efficiency is paramount for learning resource allocation with deep neural networks (DNNs) in wireless communications over highly dynamic environments. Incorporating domain knowledge into learning is a …
Accelerating Deep Reinforcement Learning With the Aid of Partial Model: Energy-Efficient Predictive Video Streaming
Predictive power allocation is conceived for energy-efficient video streaming over mobile networks using deep reinforcement learning. The goal is to minimize the accumulated energy consumption of each base station over a…
Deep Reinforcement LearningReinforcement LearningOptimal Power Allocation for Rate Splitting Communications with Deep Reinforcement Learning
This letter introduces a novel framework to optimize the power allocation for users in a Rate Splitting Multiple Access (RSMA) network. In the network, messages intended for users are split into different parts that are …
Deep Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes
Determinantal point processes (DPPs) have attracted significant attention in machine learning for their ability to model subsets drawn from a large item collection. Recent work shows that nonsymmetric DPP (NDPP) kernels …
Point Processes