We show that, in many settings, the worst-case performance of a distributed optimization algorithm is independent of the number of agents in the system, and can thus be computed in the fundamental case with just two agents. This result relies on a novel approach that systematically exploits symmetries in worst-case performance computation, framed as Semidefinite Programming (SDP) via the Performance Estimation Problem (PEP) framework. Harnessing agent symmetries in the PEP yields compact problems whose size is independent of the number of agents in the system. When all agents are equivalent in the problem, we establish the explicit conditions under which the resulting worst-case performance is independent of the number of agents and is therefore equivalent to the basic case with two agents. Our compact PEP formulation also allows the consideration of multiple equivalence classes of agents, and its size only depends on the number of equivalence classes. This enables practical and automated performance analysis of distributed algorithms in numerous complex and realistic settings, such as the analysis of the worst agent performance. We leverage this new tool to analyze the performance of the EXTRA algorithm in advanced settings and its scalability with the number of agents, providing a tighter analysis and deeper understanding of the algorithm performance.

Active traffic management incorporating autonomous vehicles (AVs) promises a future with diminished congestion and enhanced traffic flow. However, developing algorithms for real-world application requires addressing the challenges posed by continuous traffic flow and partial observability. To bridge this gap and advance the field of active traffic management towards greater decentralization, we introduce a novel asymmetric actor-critic model aimed at learning decentralized cooperative driving policies for autonomous vehicles using single-agent reinforcement learning. Our approach employs attention neural networks with masking to handle the dynamic nature of real-world traffic flow and partial observability. Through extensive evaluations against baseline controllers across various traffic scenarios, our model shows great potential for improving traffic flow at diverse bottleneck locations within the road system. Additionally, we explore the challenge associated with the conservative driving behaviors of autonomous vehicles that adhere strictly to traffic regulations. The experiment results illustrate that our proposed cooperative policy can mitigate potential traffic slowdowns without compromising safety.

Managing delivery deadlines in automated warehouses and factories is crucial for maintaining customer satisfaction and ensuring seamless production. This study introduces the problem of online multi-agent pickup and delivery with task deadlines (MAPD-D), which is an advanced variant of the online MAPD problem incorporating delivery deadlines. MAPD-D presents a dynamic deadline-driven approach that includes task deadlines, with tasks being added at any time (online), thus challenging conventional MAPD frameworks. To tackle MAPD-D, we propose a novel algorithm named deadline-aware token passing (D-TP). The D-TP algorithm is designed to calculate pickup deadlines and assign tasks while balancing execution cost and deadline proximity. Additionally, we introduce the D-TP with task swaps (D-TPTS) method to further reduce task tardiness, enhancing flexibility and efficiency via task-swapping strategies. Numerical experiments were conducted in simulated warehouse environments to showcase the effectiveness of the proposed methods. Both D-TP and D-TPTS demonstrate significant reductions in task tardiness compared to existing methods, thereby contributing to efficient operations in automated warehouses and factories with delivery deadlines.

The ability to predict the future trajectories of traffic participants is crucial for the safe and efficient operation of autonomous vehicles. In this paper, a diffusion-based generative model for multi-agent trajectory prediction is proposed. The model is capable of capturing the complex interactions between traffic participants and the environment, accurately learning the multimodal nature of the data. The effectiveness of the approach is assessed on large-scale datasets of real-world traffic scenarios, showing that our model outperforms several well-established methods in terms of prediction accuracy. By the incorporation of differential motion constraints on the model output, we illustrate that our model is capable of generating a diverse set of realistic future trajectories. Through the use of an interaction-aware guidance signal, we further demonstrate that the model can be adapted to predict the behavior of less cooperative agents, emphasizing its practical applicability under uncertain traffic conditions.

We present alternative approaches to routing and scheduling in Answer Set Programming (ASP), and explore them in the context of Multi-agent Path Finding. The idea is to capture the flow of time in terms of partial orders rather than time steps attached to actions and fluents. This also abolishes the need for fixed upper bounds on the length of plans. The trade-off for this avoidance is that (parts of) temporal trajectories must be acyclic, since multiple occurrences of the same action or fluent cannot be distinguished anymore. While this approach provides an interesting alternative for modeling routing, it is without alternative for scheduling since fine-grained timings cannot be represented in ASP in a feasible way. This is different for partial orders that can be efficiently handled by external means such as acyclicity and difference constraints. We formally elaborate upon this idea and present several resulting ASP encodings. Finally, we demonstrate their effectiveness via an empirical analysis.

In environments where many automated guided vehicles (AGVs) operate, planning efficient, collision-free paths is essential. Related research has mainly focused on environments with static passages, resulting in space inefficiency. We define multi-agent and multi-rack path finding (MARPF) as the problem of planning paths for AGVs to convey target racks to their designated locations in environments without passages. In such environments, an AGV without a rack can pass under racks, whereas an AGV with a rack cannot pass under racks to avoid collisions. MARPF entails conveying the target racks without collisions, while the other obstacle racks are positioned without a specific arrangement. AGVs are essential for relocating other racks to prevent any interference with the target racks. We formulated MARPF as an integer linear programming problem in a network flow. To distinguish situations in which an AGV is or is not loading a rack, the proposed method introduces two virtual layers into the network. We optimized the AGVs' movements to move obstacle racks and convey the target racks. The formulation and applicability of the algorithm were validated through numerical experiments. The results indicated that the proposed algorithm addressed issues in environments with dense racks.

This paper proposes an adaptive behavioral decision-making method for autonomous vehicles (AVs) focusing on complex merging scenarios. Leveraging principles from non-cooperative game theory, we develop a vehicle interaction behavior model that defines key traffic elements and integrates a multifactorial reward function. Maximum entropy inverse reinforcement learning (IRL) is employed for behavior model parameter optimization. Optimal matching parameters can be obtained using the interaction behavior feature vector and the behavior probabilities output by the vehicle interaction model. Further, a behavioral decision-making method adapted to dynamic environments is proposed. By establishing a mapping model between multiple environmental variables and model parameters, it enables parameters online learning and recognition, and achieves to output interactive behavior probabilities of AVs. Quantitative analysis employing naturalistic driving datasets (highD and exiD) and real-vehicle test data validates the model's high consistency with human decision-making. In 188 tested interaction scenarios, the average human-like similarity rate is 81.73%, with a notable 83.12% in the highD dataset. Furthermore, in 145 dynamic interactions, the method matches human decisions at 77.12%, with 6913 consistence instances. Moreover, in real-vehicle tests, a 72.73% similarity with 0% safety violations are obtained. Results demonstrate the effectiveness of our proposed method in enabling AVs to make informed adaptive behavior decisions in interactive environments.

Generating competitive strategies and performing continuous motion planning simultaneously in an adversarial setting is a challenging problem. In addition, understanding the intent of other agents is crucial to deploying autonomous systems in adversarial multi-agent environments. Existing approaches either discretize agent action by grouping similar control inputs, sacrificing performance in motion planning, or plan in uninterpretable latent spaces, producing hard-to-understand agent behaviors. This paper proposes an agent strategy representation via Policy Characteristic Space that maps the agent policies to a pre-specified low-dimensional space. Policy Characteristic Space enables the discretization of agent policy switchings while preserving continuity in control. Also, it provides intepretability of agent policies and clear intentions of policy switchings. Then, regret-based game-theoretic approaches can be applied in the Policy Characteristic Space to obtain high performance in adversarial environments. Our proposed method is assessed by conducting experiments in an autonomous racing scenario using scaled vehicles. Statistical evidence shows that our method significantly improves the win rate of ego agent and the method also generalizes well to unseen environments.

Coalition logic is a central logic in strategic reasoning studies. In this paper, we first argue that Coalition Logic models, concurrent game models, have three too-strong assumptions. The first one is the independence of agents; that is, the merge of two available joint actions of two disjoint coalitions is always available for the union of the two coalitions. The second one is seriality; that is, coalitions always have available joint actions. The third one is determinism, that is, the grand coalition's joint actions always have a unique outcome. Second, we present a coalition logic based on general concurrent game models, which do not have the three assumptions. We show the completeness of this logic and compare it with Coalition Logic in detail. This logic seems minimal in the context of strategic reasoning.

We address in this paper Reinforcement Learning (RL) among agents that are grouped into teams such that there is cooperation within each team but general-sum (non-zero sum) competition across different teams. To develop an RL method that provably achieves a Nash equilibrium, we focus on a linear-quadratic structure. Moreover, to tackle the non-stationarity induced by multi-agent interactions in the finite population setting, we consider the case where the number of agents within each team is infinite, i.e., the mean-field setting. This results in a General-Sum LQ Mean-Field Type Game (GS-MFTGs). We characterize the Nash equilibrium (NE) of the GS-MFTG, under a standard invertibility condition. This MFTG NE is then shown to be $\mathcal{O}(1/M)$-NE for the finite population game where $M$ is a lower bound on the number of agents in each team. These structural results motivate an algorithm called Multi-player Receding-horizon Natural Policy Gradient (MRPG), where each team minimizes its cumulative cost independently in a receding-horizon manner. Despite the non-convexity of the problem, we establish that the resulting algorithm converges to a global NE through a novel problem decomposition into sub-problems using backward recursive discrete-time Hamilton-Jacobi-Isaacs (HJI) equations, in which independent natural policy gradient is shown to exhibit linear convergence under time-independent diagonal dominance. Experiments illuminate the merits of this approach in practice.