Multiaccess edge computing (MEC) has emerged as a promising solution that can enable low-end terminal devices to run large complex applications by offloading their tasks to edge servers. The task offloading strategy, determining how to offload tasks, remains the most critical issue of MEC. Traditional offloading approaches either suffer from high computational complexity or poor self-adjustability to dynamic changes in the edge environment. Deep reinforcement learning (DRL) provides an effective way to tackle these issues. However, most existing DRL-based methods solely consider either a continuous or a discrete action space, where the limited action space results in accuracy loss and restricts the optimality of offloading decisions. Nevertheless, the edge task offloading problem in practice often confronts both discrete and continuous actions. In this article, we propose a tailored proximal policy optimization (PPO)-based method, named Hybrid-PPO, enhanced by the parameterized discrete-continuous hybrid action space. Assisted with Hybrid-PPO, we further design a novel DRL-based multiserver multitask collaborative partial task offloading scheme adhering to a series of specifically built formal models. Experimental results prove that our approach achieves high offloading efficiency and outperforms the existing state-of-the-art offloading schemes in terms of convergence rate, energy cost, time cost, and generalizability under various network conditions.

The tremendous expansion of edge data traffic poses great challenges to network bandwidth and service responsiveness for mobile computing. Edge caching has emerged as a promising method to alleviate these issues by storing a portion of data at the network edge. However, existing caching approaches suffer from either poor caching efficiency with low content-hit ratio or unintelligence of caching policies lacking self-adjustability. In this paper, we propose ICE, a novel Intelligent Edge Caching scheme using a deep reinforcement learning (DRL) method to capture specific valuable information from the requested data. With the benefit of our proposed popularity model based on Newton's law of cooling, ICE fully takes into account the popularity of the contents to be cached and leverages the formulated Markov decision model to decide whether or not the contents should be cached. Moreover, to further improve the caching efficiency, we propose a novel distributed multi-node caching framework, named DCCC, assisted by a multi-tiered caching hierarchy. Comprehensive experiments show that the single-node ICE scheme greatly improves the cache hit rate and contents exchanging time in comparison with both DRL-based and legacy approaches, and our distributed multi-node caching scheme DCCC further significantly improves the overall utilization of caching space.

In this article, a speed coupled lateral strategy is developed to enhance the tracking accuracy of unmanned ground vehicles (UGVs) with variable speed. The characteristics of UGVs are described in a linear parameter-varying (LPV) path-following model, where the speed variation is treated as parameter perturbation. Based on this, a model predictive control (MPC) algorithm is put forward to calculate the desired angular velocity as the upper control input. Moreover, in order to obtain the steering wheel angle driving UGVs, an appurtenant convertor based on kinematic model is designed for the transformation of control signals. The recursive feasibility of MPC is guaranteed via a terminal constraint-free approach, while its stability is deduced through min-max approach. Finally, several experiments are conducted to demonstrate the superiority of speed coupled lateral strategy.

As a critical concern of multi-access edge computing (MEC), task offloading has received extensive attention. Although deep reinforcement learning (DRL) has achieved great success in resolving the task offloading problem, most existing DRL-based offloading schemes only consider either continuous action space or discrete action space, which results in the loss of optimality of decisions. Moreover, the generalization ability of the existing schemes is still far from adaptive to dynamic changes in the environment. This leads to offloading strategies having to conduct re-sampling and re-training, which largely impairs the offloading efficiency. To address these issues, we propose a novel efficient MEC task offloading scheme based on parameterized meta-reinforcement learning taking hybrid action space into account. We first formulate this problem as a non-convex multi-objective optimization problem. Then, we design a parameterized meta-reinforcement learning algorithm, named Meta-Hybrid-PPO, with hybrid action space to solve the optimization problem. Comprehensive experimental results show that our Meta-Hybrid-PPO not only performs better than existing state-of-the-art methods in reducing task processing latency and computational energy consumption but also achieves better adaptability.