… Inspired by the success of Deep Reinforcement Learning (DRL) in solving … for content
caching decisions at an edge node, eg, a base station. Endusers keep requesting content from the …

… , we design a deep reinforcement learning (DRL)-based cooperative caching scheme to …
Particularly, we leverage a deep deterministic policy gradient (DDPG) learning algorithm to …

… cache content or resource allocation, this article comprehensively focuses on the jointly content
placement and content delivery policy of cache… the users’ mobility and content popularity. …

… , we study content caching at the wireless network edge using a deep reinforcement learning
… In particular, we propose deep actorcritic reinforcement learning based policies for both …

… for intelligent and secure content caching. We first propose a blockchain empowered
distributed content caching framework where vehicles perform content caching and base stations …

… In the content update phase, we propose to update the BS cache by taking into account
both the newly fetched contents and its cache in current time slot. The BS first evicts or retains …

… -driven D2D offloading and content caching process is … Content Service Provider (CSP). To
solve the optimization problem, the content caching method based on a Deep Reinforcement …

… s content delivery networks such as Akamai [32], have tree network structures. Accounting for
the hierarchy of caches … Joint routing and innetwork content caching in a hierarchical cache …

… proactive content caching (FPC) can alleviate the matter by placing content in local cache
to … -efficient FPC policy to improve the content caching efficiency and reduce the resources …

… a set of video content to cache for each decision. To address this problem, we define the
output space of each actor network as the caching probability of each content and the top Ce …