Zhiyong Chen

In this paper, a novel distributed braking scheme is proposed for automatic heavy-haul trains equipped with an electronically controlled pneumatic (ECP) braking system. The scheme consists of a coupler force compensator and a cooperative controller. The compensator is designed to counteract the coupler force acting on each car, i.e., the forces from its front and rear adjacent cars. With this compensation, the braking control problem is transformed into a platooning problem of multiple vehicles. The cooperative controller then regulates the velocity and position of adjacent cars. Numerical studies using MATLAB and the Universal Mechanism simulator are conducted to verify the effectiveness and superiority of the proposed scheme.

Multiagent reinforcement learning, as a prominent intelligent paradigm, enables collaborative decision-making within complex systems. However, existing approaches often rely on explicit action exchange between agents to evaluate action value functions, which is frequently impractical in real-world engineering environments due to communication constraints, latency, energy consumption, and reliability requirements.From an artificial intelligence perspective, this paper proposes an enhanced multiagent reinforcement learning framework that employs action estimation neural networks to infer agent behaviors. By integrating a lightweight action estimation module, each agent infers neighboring agents' behaviors using only locally observable information, enabling collaborative policy learning without explicit action sharing. This approach is fully compatible with standard Twin-Delay Deep Deterministic Policy Gradient (TD3) algorithms and scalable to larger multiagent systems.At the engineering application level, this framework has been implemented and validated in dual-arm robotic manipulation tasks: two robotic arms collaboratively lift objects. Experimental results demonstrate that this approach significantly enhances the robustness and deployment feasibility of real-world robotic systems while reducing dependence on information infrastructure.Overall, this research advances the development of decentralized multiagent artificial intelligence systems while enabling artificial intelligence to operate effectively in dynamic, information-constrained real-world environments.

This paper introduces a novel structure based on Kolmogorov-Arnold Networks (KANs) in reinforcement learning tasks. The adaptive and learnable activation function replaces the B-spline function, enhancing the model’s expressivity and yielding a compact structure during training. To address challenges associated with KAN’s inherent recursiveness, we integrate exponentially decaying sinusoid functions, which also enhance GPU optimization and offer an interpretable alternative to standard RL networks. Our implementation achieves comparable performance while using fewer parameters than a standard neural network, thereby reducing memory consumption. Additionally, a parameter reduction in the global stream contributes to smoother learning dynamics and a more streamlined network design. This innovation not only minimizes the learning size but also enables efficient deployment on resource-constrained devices.

Multiagent reinforcement learning (MARL) has attracted considerable attention due to its potential in addressing complex cooperative tasks. However, existing MARL approaches often rely on frequent exchanges of action or state information among agents to achieve effective coordination, which is difficult to satisfy in practical robotic systems. A common solution is to introduce estimator networks to model the behaviors of other agents and predict their actions; nevertheless, such designs cause the size and computational cost of the estimator networks to grow rapidly with the number of agents, thereby limiting scalability in large-scale systems. To address these challenges, this paper proposes a multiagent learning framework augmented with a Collective Influence Estimation Network (CIEN). By explicitly modeling the collective influence of other agents on the task object, each agent can infer critical interaction information solely from its local observations and the task object's states, enabling efficient collaboration without explicit action information exchange. The proposed framework effectively avoids network expansion as the team size increases; moreover, new agents can be incorporated without modifying the network structures of existing agents, demonstrating strong scalability. Experimental results on multiagent cooperative tasks based on the Soft Actor-Critic (SAC) algorithm show that the proposed method achieves stable and efficient coordination under communication-limited environments. Furthermore, policies trained with collective influence modeling are deployed on a real robotic platform, where experimental results indicate significantly improved robustness and deployment feasibility, along with reduced dependence on communication infrastructure.