Deep Reinforcement Learning Based Trajectory Planning Under Uncertain Constraints

Trajectory planning in complex environments with uncertain constraints is a challenging problem with numerous applications in robotics, autonomous vehicles, and aerial systems. Deep Reinforcement Learning (DRL) has emerged as a promising approach to address this issue by enabling agents to learn optimal policies through trial and error. This research paper presents a comprehensive study on employing DRL techniques for trajectory planning under uncertain constraints. We propose a novel framework that combines deep learning models with reinforcement learning algorithms to generate safe and efficient trajectories while accounting for environmental uncertainties. The performance of the proposed approach is evaluated through simulations and real-world scenarios, showcasing its effectiveness in handling various uncertainty sources and providing robust trajectory planning solutions.