With the development of unmanned aerial vehicle (UAV) technology, UAV swarm confrontation has attracted many researchers’ attention. However, the situation faced by the UAV swarm has substantial uncertainty and dynamic variability. The state space and action space increase exponentially with the number of UAVs, so that autonomous decision-making becomes a difficult problem in the confrontation environment. In this paper, a multiagent reinforcement learning method with macro action and human expertise is proposed for autonomous decision-making of UAVs. In the proposed approach, UAV swarm is modeled as a large multiagent system (MAS) with an individual UAV as an agent, and the sequential decision-making problem in swarm confrontation is modeled as a Markov decision process. Agents in the proposed method are trained based on the macro actions, where sparse and delayed rewards, large state space, and action space are effectively overcome. The key to the success of this method is the generation of the macro actions that allow the high-level policy to find a near-optimal solution. In this paper, we further leverage human expertise to design a set of good macro actions. Extensive empirical experiments in our constructed swarm confrontation environment show that our method performs better than the other algorithms.

With the increasing presence of blockchain-based distributed applications in various aspects of daily life, there has been a growing focus on the privacy protection of blockchain ledgers and the corresponding regulatory technologies. However, current mainstream solutions primarily concentrate on the verifiable encryption of blockchain transaction addresses and contents, neglecting the regulatory requirements for private transactions. Moreover, the few monitorable solutions suffer from issues such as excessive centralization and a single-minded approach to regulatory content. To address these deficiencies, this paper proposes a blockchain privacy-preserving scheme that supports multi-level regulation through the utilization of zero-knowledge proofs (zk-SNARKs) and attribute-based encryption (ABE). Firstly, by leveraging zk-SNARKs, this scheme achieves blockchain privacy-preserving within an account model, enabling the concealment of user transaction addresses and values. Secondly, by employing attribute-based encryption, a multi-level regulatory model is developed alongside the privacy protection measures, allowing for selective disclosure of transaction content. Finally, through a combination of various technical approaches, the efficiency of this model is enhanced, and its usability is validated through experimental verification.