Recent advancements in multi-robot navigation have explored methods that combine Large Language Models (LLMs) for tasks like scene understanding or high-level decision-making. However, these approaches face challenges with high inference latency and potential hallucinations. To address these challenges, we propose a knowledge-driven Reinforcement Learning (RL) framework, GUIDER, that utilizes an LLM in two different offline roles. First, we leverage the LLM as an offline knowledge source. Its expertise is distilled into a compact model, which is applied only when the RL agent is uncertain about its own value estimates and the model itself is confident in its prediction. Additionally, we utilize the LLM as an offline semantic engine. This process translates the LLM's high-level understanding of situational risk into a dynamic adjustment of the RL agent's behavioral style, evolving a function that optimally balances conservative and aggressive actions. We conduct extensive experiments in both terrestrial and maritime settings. Across all maritime scenarios (3–12 robots), GUIDER improves the task success rate and reduces the collision rate significantly compared to the state-of-the-art RL-based multi-robot navigation methods.

In highly interactive driving environments, the collision accidents and severe congestion often occur due to the multi-modal driving behaviors and dynamic interactions of surrounding vehicles. Moreover, the balance between individual and overall traffic is hard to maintain as well. Such challenging scenarios have become a major obstacle for its real-world application. Owing to the significant advancements of the IoT and AI, multi agent method and the awareness of driving risk have become promising way to enhance the safety problems. To improve the safety and efficiency in highly dynamic and interactive environment, a predictive risk-aware cooperative driving policy is proposed based on multi-agent reinforcement learning (MARL) framework. Firstly, considering multiple future behaviors and dynamic evolution of surrounding vehicles, a predictive risk field is established based on elliptical collision boundary. And then, the predictive risk is introduced in our cooperative driving policy to enhance the safety in highly interactive environment. Furthermore, efficiency of individual vehicle and overall traffic flow are balanced when we design the reward function. Subsequently, to learn the optimal cooperative driving policy, an advantage actor-critic algorithm is also designed, in which the predictive risk is integrated into the centralized critic, facilitating comprehension of global traffic situation and coordinate all vehicles to ensure safety. Moreover, the risk-aware critic guides the policy updating of decentralized actor for each controlled vehicle. In this way, our approach could improve the comprehension of interactive and dynamic traffic environment. The results demonstrate that the introduction of predictive risk and multi-agent systems can make a trade off between individual and overall traffic efficiency within safety constraints, even in challenging traffic situations with high-density flows.