Connected and autonomous vehicles across land, water, and air must often operate in dynamic, unpredictable environments with limited communication, no centralized control, and partial observability. These real-world constraints pose significant challenges for coordination, particularly when vehicles pursue individual objectives. To address this, we propose a decentralized Multi-Agent Reinforcement Learning (MARL) framework that enables vehicles, acting as agents, to communicate selectively based on local goals and observations. This goal-aware communication strategy allows agents to share only relevant information, enhancing collaboration while respecting visibility limitations. We validate our approach in complex multi-agent navigation tasks featuring obstacles and dynamic agent populations. Results show that our method significantly improves task success rates and reduces time-to-goal compared to non-cooperative baselines. Moreover, task performance remains stable as the number of agents increases, demonstrating scalability. These findings highlight the potential of decentralized, goal-driven MARL to support effective coordination in realistic multi-vehicle systems operating across diverse domains.

However, approximating policies using deep neural networks makes it difficult to interpret the learned decision-making process.

Multi-Agent Path Finding (MAPF) poses a significant and challenging problem critical for applications in robotics and logistics, particularly due to its combinatorial complexity and the partial observability inherent in realistic environments. Decentralized reinforcement learning methods commonly encounter two substantial difficulties: first, they often yield self-centered behaviors among agents, resulting in frequent collisions, and second, their reliance on complex communication modules leads to prolonged training times, sometimes spanning weeks. To address these challenges, we propose an efficient decentralized planning framework based on the Decision Transformer (DT), uniquely leveraging offline reinforcement learning to substantially reduce training durations from weeks to mere hours. Crucially, our approach effectively handles long-horizon credit assignment and significantly improves performance in scenarios with sparse and delayed rewards. Furthermore, to overcome adaptability limitations inherent in standard RL methods under dynamic environmental changes, we integrate a large language model (GPT-4o) to dynamically guide agent policies. Extensive experiments in both static and dynamically changing environments demonstrate that our DT-based approach, augmented briefly by GPT-4o, significantly enhances adaptability and performance.

When regarding the suffering of others, we often experience personal distress and feel compelled to help\footnote{Preprint. Under review.}. Inspired by living systems, we investigate the emergence of prosocial behavior among autonomous agents that are motivated by homeostatic self-regulation. We perform multi-agent reinforcement learning, treating each agent as a vulnerable homeostat charged with maintaining its own well-being. We introduce an empathy-like mechanism to share homeostatic states between agents: an agent can either \emph{observe} their partner's internal state ({\bf cognitive empathy}) or the agent's internal state can be \emph{directly coupled} to that of their partner ({\bf affective empathy}). In three simple multi-agent environments, we show that prosocial behavior arises only under homeostatic coupling - when the distress of a partner can affect one's own well-being. Additionally, we show that empathy can be learned: agents can ``decode" their partner's external emotive states to infer the partner's internal homeostatic states. Assuming some level of physiological similarity, agents reference their own emotion-generation functions to invert the mapping from outward display to internal state. Overall, we demonstrate the emergence of prosocial behavior when homeostatic agents learn to ``read" the emotions of others and then to empathize, or feel as they feel.

Autonomous navigation is reshaping various domains in people's life by enabling efficient and safe movement in complex environments. Reliable navigation requires algorithmic approaches that compute optimal or near-optimal trajectories while satisfying task-specific constraints and ensuring obstacle avoidance. However, existing methods struggle with slow convergence and suboptimal solutions, particularly in complex environments, limiting their real-world applicability. To address these limitations, this paper presents the Multi-Strategy Enhanced Crayfish Optimization Algorithm (MCOA), a novel approach integrating three key strategies: 1) Refractive Opposition Learning, enhancing population diversity and global exploration, 2) Stochastic Centroid-Guided Exploration, balancing global and local search to prevent premature convergence, and 3) Adaptive Competition-Based Selection, dynamically adjusting selection pressure for faster convergence and improved solution quality. Empirical evaluations underscore the remarkable planning speed and the amazing solution quality of MCOA in both 3D Unmanned Aerial Vehicle (UAV) and 2D mobile robot path planning. Against 11 baseline algorithms, MCOA achieved a 69.2% reduction in computational time and a 16.7% improvement in minimizing overall path cost in 3D UAV scenarios. Furthermore, in 2D path planning, MCOA outperformed baseline approaches by 44% on average, with an impressive 75.6% advantage in the largest 60*60 grid setting. These findings validate MCOA as a powerful tool for optimizing autonomous navigation in complex environments. The source code is available at: https://github.com/coedv-hub/MCOA.

When regarding the suffering of others, we often experience personal distress and feel compelled to help. Inspired by living systems, we investigate the emergence of prosocial behavior among autonomous agents that are motivated by homeostatic self-regulation. We perform multi-agent reinforcement learning, treating each agent as a vulnerable homeostat charged with maintaining its own well-being. We introduce an empathy-like mechanism to share homeostatic states between agents: an agent can either \emph{observe} their partner's internal state (cognitive empathy) or the agent's internal state can be \emph{directly coupled} to that of their partner's (affective empathy). In three simple multi-agent environments, we show that prosocial behavior arises only under homeostatic coupling - when the distress of a partner can affect one's own well-being. Our findings specify the type and role of empathy in artificial agents capable of prosocial behavior.

Spatial reasoning, an important faculty of human cognition with many practical applications, is one of the core commonsense skills that is not purely language-based and, for satisfying (as opposed to optimal) solutions, requires some minimum degree of planning. Existing benchmarks of Commonsense Spatial Reasoning (CSR) tend to evaluate how Large Language Models (LLMs) interpret text-based spatial descriptions rather than directly evaluate a plan produced by the LLM in response to a spatial reasoning scenario. In this paper, we construct a large-scale benchmark called $\textbf{GRASP}$, which consists of 16,000 grid-based environments where the agent is tasked with an energy collection problem. These environments include 100 grid instances instantiated using each of the 160 different grid settings, involving five different energy distributions, two modes of agent starting position, and two distinct obstacle configurations, as well as three kinds of agent constraints. Using GRASP, we compare classic baseline approaches, such as random walk and greedy search methods, with advanced LLMs like GPT-3.5-Turbo and GPT-4o. The experimental results indicate that even these advanced LLMs struggle to consistently achieve satisfactory solutions.

We anticipate increased instances of humans and AI systems working together in what we refer to as a hybrid team. The increase in collaboration is expected as AI systems gain proficiency and their adoption becomes more widespread. However, their behavior is not error-free, making hybrid teams a very suitable solution. As such, we consider methods for improving performance for these teams of humans and AI systems. For hybrid teams, we will refer to both the humans and AI systems as agents. To improve team performance over that seen for agents operating individually, we propose a manager which learns, through a standard Reinforcement Learning scheme, how to best delegate, over time, the responsibility of taking a decision to any of the agents. We further guide the manager's learning so they also minimize how many changes in delegation are made resulting from undesirable team behavior. We demonstrate the optimality of our manager's performance in several grid environments which include failure states which terminate an episode and should be avoided. We perform our experiments with teams of agents with varying degrees of acceptable risk, in the form of proximity to a failure state, and measure the manager's ability to make effective delegation decisions with respect to its own risk-based constraints, then compare these to the optimal decisions. Our results show our manager can successfully learn desirable delegations which result in team paths near/exactly optimal with respect to path length and number of delegations.

This paper introduces a novel algorithm for solving the point-to-point shortest path problem in a static regular 8-neighbor connectivity (G8) grid. This algorithm can be seen as a generalization of Hadlock algorithm to G8 grids, and is shown to be theoretically equivalent to the relaxed $A^*$ ($RA^*$) algorithm in terms of the provided solution's path length, but with substantial time and memory savings, due to a completely different computation strategy, based on defining a set of lookup matrices. Through an experimental study on grid maps of various types and sizes (1290 runs on 43 maps), it is proven to be 2.25 times faster than $RA^*$ and 17 times faster than the original $A^*$, in average. Moreover, it is more memory-efficient, since it does not need to store a G score matrix.