We propose a caching policy that uses a feedforward neural network (FNN) to predict content popularity. Our scheme outperforms popular eviction policies like LRU or ARC, but also a new policy relying on the more complex recurrent neural networks. At the same time, replacing the FNN predictor with a naive linear estimator does not degrade caching performance significantly, questioning then the role of neural networks for these applications.
Another set of papers like @cite_9 @cite_0 use reinforcement learning to adapt dynamically the caching policy. A reinforcement learning algorithm tries to learn directly the best action to pursue, often by a trial-and-error approach. For this reason, training a reinforcement learning algorithm is often more complex than learning the parameters of a neural network, and results may be unstable @cite_17 .
@cite_0 @cite_9 @cite_17
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Caching networks are designed to reduce traffic load at backhaul links, by serving demands from edge-nodes. In the past decades, many studies have been done to address the caching problem. However, in practice, finding an optimal caching policy is still challenging due to dynamicity of traffic and scalability caused by complex impact of caching strategy chosen by each individual cache on other parts of network. In this paper, we focus on cache placement to optimize the performance metrics such as hit ratio in cooperative large-scale caching networks. Our proposed solution, cooperative multi-agent based cache placement (CoM-Cache) is based on multi-agent reinforcement learning framework and can seamlessly track the content popularity dynamics in an on-line fashion. CoM-Cache is enable to solve the problems over a spectrum from isolated to interconnected caches and is designed flexibly to fit any caching networks. To deal with dimensionality issue, CoM-Cache exploits the property of locality of interactions among caches. The experimental results report CoM-Cache outperforms base-line schemes, however at the expense of reasonable additional complexity. 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-term cache hit rate, and it requires no knowledge of the content popularity distribution. To evaluate the proposed framework, we compare the performance with other caching algorithms, including Least Recently Used (LRU), Least Frequently Used (LFU), and First-In First-Out (FIFO) caching strategies. Meanwhile, since the Wolpertinger architecture can effectively limit the action space size, we also compare the performance with Deep Q-Network to identify the impact of dropping a portion of the actions. Our results show that the proposed framework can achieve improved short-term cache hit rate and improved and stable long-term cache hit rate in comparison with LRU, LFU, and FIFO schemes. Additionally, the performance is shown to be competitive in comparison to Deep Q-learning, while the proposed framework can provide significant savings in runtime. In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to sustaining this progress. Unfortunately, reproducing results for state-of-the-art deep RL methods is seldom straightforward. In particular, non-determinism in standard benchmark environments, combined with variance intrinsic to the methods, can make reported results tough to interpret. Without significance metrics and tighter standardization of experimental reporting, it is difficult to determine whether improvements over the prior state-of-the-art are meaningful. In this paper, we investigate challenges posed by reproducibility, proper experimental techniques, and reporting procedures. We illustrate the variability in reported metrics and results when comparing against common baselines and suggest guidelines to make future results in deep RL more reproducible. We aim to spur discussion about how to ensure continued progress in the field by minimizing wasted effort stemming from results that are non-reproducible and easily misinterpreted.