Deep Reinforcement Learning With Multicritic TD3 for Decentralized Multirobot Path Planning

Abstract

Centralized multirobot path planning is a prevalent approach involving a global planner computing feasible paths for each robot using shared information. Nonetheless, this approach encounters limitations due to communication constraints and computational complexity. To address these challenges, we introduce a novel decentralized multirobot path planning approach that eliminates the need for sharing the states and intentions of robots. Our approach harnesses deep reinforcement learning and features an asynchronous multicritic twin delayed deep deterministic policy gradient (AMC-TD3) algorithm, which enhances the original gate recurrent unit (GRU)-attention-based TD3 algorithm by incorporating a multicritic network and employing an asynchronous training mechanism. By training each critic with a unique reward function, our learned policy enables each robot to navigate toward its long-term objective without colliding with other robots in complex environments. Furthermore, our reward function, grounded in social norms, allows the robots to naturally avoid each other in congested situations. Specifically, we train three critics to encourage each robot to achieve its long-term navigation goal, maintain its moving direction, and prevent collisions with other robots. Our model can learn an end-to-end navigation policy without relying on an accurate map or any localization information, rendering it highly adaptable to various environments. Simulation results reveal that our proposed approach surpasses baselines in several environments with different levels of complexity and robot populations.