论文标题
将基于命题逻辑的决策图与城市系统中的决策结合在一起
Combining Propositional Logic Based Decision Diagrams with Decision Making in Urban Systems
论文作者
论文摘要
由于环境中的不确定性，部分可观察性和当前问题的可扩展性，解决多重问题可能是一项艰巨的任务。尤其是在城市环境中，面临更多的挑战，因为我们还需要维持所有用户的安全，同时最大程度地减少了代理商的拥塞以及旅行时间。为此，我们解决了在不确定性和部分可观察性下进行多种探路问题的问题，在这些问题中，代理人的任务是从起点转移到终点，同时还满足了一些限制，例如低拥塞，并将其作为多种强化的强化学习问题。我们使用命题逻辑来编译域约束，并将它们与RL算法集成在一起，以启用RL快速模拟。
Solving multiagent problems can be an uphill task due to uncertainty in the environment, partial observability, and scalability of the problem at hand. Especially in an urban setting, there are more challenges since we also need to maintain safety for all users while minimizing congestion of the agents as well as their travel times. To this end, we tackle the problem of multiagent pathfinding under uncertainty and partial observability where the agents are tasked to move from their starting points to ending points while also satisfying some constraints, e.g., low congestion, and model it as a multiagent reinforcement learning problem. We compile the domain constraints using propositional logic and integrate them with the RL algorithms to enable fast simulation for RL.
