Effective energy management of electric vehicle (EV) charging stations is critical to supporting the transport sector's sustainable energy transition. This paper addresses the EV charging coordination by considering vehicle- to- vehicle (V2V) energy exchange as the flexibility to harness in EV charging stations. Moreover, this paper takes into account EV user experiences, such as charging satisfaction and fairness. We propose a Multi-Agent Reinforcement Learning (MARL) approach to coordinate EV charging with V2V energy exchange while considering uncertainties in the EV arrival time, energy price, and solar energy generation. The exploration capability of MARL is enhanced by introducing parameter noise into MARL's neural network models. Experimental results demonstrate the superior performance and scalability of our proposed method compared to traditional optimization baselines. The decentralized execution of the algorithm enables it to effectively deal with partial system faults in the charging station.

Fuzzing is widely used in vulnerability mining because of its simplicity and efficiency. The fuzzing tool generates numerous testcases according to the mutation of the initial seed and inputs them into the program to be tested. At the same time, it monitors exceptions of the running program to find possible software bugs. In order to improve the performance of fuzzing, many researchers are committed to generating various initial testcases. However, at present, fuzzing testcase generation does not make full use of the information of testcases, the generation process is uncontrollable, and the generation effect is general. In view of the problems mentioned above, this paper proposes a testcase generation method based on conditional generative adversarial networks. The CWGAN-GP (Conditional Wasserstein Generic Adversarial Network-Gradient Penalty) learns the format features of testcases, and generates testcases covering corresponding branches according to the input branch information. This paper conducted experiments on 6 common programs, and the experiments showed that compared to the initial training set, testcases generated by (C)WGAN-GP can extend the exploration of the program, and thus find more vulnerabilities. The CWGAN-GP model uses the branch vector in the training set as the condition to generate testcases, which can find more crashes and hangs, and improve the final fuzzing performance.

In fuzzing, the generative adversarial network learns from the training set and generates test-cases with similar formats, so as to provide inputs conforming to the input format for programs tested. However, problems of the unstable training process, single generation method, and monotonic sample types generated exist in general generative adversarial networks. This paper proposes a fuzzing input generation technique based on VAE-GAN, which introduces the representation learning process of variational auto-encoder for traditional generative adversarial networks, so that it can learn and utilize the character information of testcases, improving the stability of training and generate various testcases. It is shown that testcases generated by VAE-GAN trigger more unique tuples than other existing generative adversarial networks on 3 among 4 selected target programs. Moreover, compared with the AFL mutation training set, testcases generated by VAE-GAN can improve code coverage by up to 11.87%, and the discovery rate of 15.74% and 5.36% in the unique crashes and hangs respectively.