Cache-Enabled Dynamic Rate Allocation via Deep Self-Transfer Reinforcement Learning

Cache-enabled Wireless Networks with Opportunistic Interference Alignment

Both caching and interference alignment (IA) are promising techniques for future wireless networks. Nevertheless, most of existing works on cache-enabled IA wireless networks assume that the channel is invariant, which is unrealistic considering the time-varying nature of practical wireless environments. In this paper, we consider realistic time-varying channels. Specifically, the channel is fo...

A Deep Reinforcement Learning-Based Framework for Content Caching

Content caching at the edge nodes is a promising technique to reduce the data traffic in next-generation wireless networks. Inspired by the success of Deep Reinforcement Learning (DRL) in solving complicated control problems, this work presents a DRL-based framework with Wolpertinger architecture for content caching at the base station. The proposed framework is aimed at maximizing the long-ter...

Dynamic Resource Allocation with Integrated Reinforcement Learning for a D2D-Enabled LTE-A Network with Access to Unlicensed Band

Operation Scheduling of MGs Based on Deep Reinforcement Learning Algorithm

: In this paper, the operation scheduling of Microgrids (MGs), including Distributed Energy Resources (DERs) and Energy Storage Systems (ESSs), is proposed using a Deep Reinforcement Learning (DRL) based approach. Due to the dynamic characteristic of the problem, it firstly is formulated as a Markov Decision Process (MDP). Next, Deep Deterministic Policy Gradient (DDPG) algorithm is presented t...

Bacteria Foraging Reinforcement Learning for Risk-Based Economic Dispatch via Knowledge Transfer

This paper proposes a novel bacteria foraging reinforcement learning with knowledge transfer method for risk-based economic dispatch, in which the economic dispatch is integrated with risk assessment theory to represent the uncertainties of active power demand and contingencies during power system operations. Moreover, a multi-agent collaboration is employed to accelerate the convergence of kno...