6 天前 - … In this paper, we introduce an apprentice learning approach in which the learner
agents follow the formation and trajectory of the expert agents. The importance of maintaining …

8 天前 - … an innovative trajectory planning algorithm for stratospheric airships, combining
Artificial Potential Field (APF) method with Soft Actor-Critic (SAC) deep reinforcement learning …

9 天前 - … trajectory planning and resource allocation scheme based on hierarchical deep
reinforcement learning … sub-problems: high-level global trajectory planning and low-level local …

9 天前 - … consists of a deep reinforcement learning (DRL) based … a method for training a
reinforcement learning (RL) agent … to be predictive of obstacle trajectories. A comparison study …

10 天前 - … RoboGen simultaneously employs motion planningbased primitives, gradient-based
trajectory optimization, and reinforcement learning to acquire skills. For each task, we …

11 天前 - … to time-varying semantic information, and to perform queries in sub-microsecond,
enabling applications in reinforcement learning, trajectory optimization, and motion planning. …

13 天前 - … , various trajectory planning … reinforcement learning algorithm based on an
end-to-end mapless collision avoidance algorithm for training in the proposed distributed learning …

13 天前 - … to generate intelligent planned behavior. Although reinforcement learning methods
that … gap by developing algorithms that compute trajectories in real-time while converging …

13 天前 - … This assumption simplifies the problem compared to the rewardfree RL framework
considered in this work, where the agent must follow the MDP trajectory within each episode …

14 天前 - … to adapt to time-varying semantic information, and to perform queries in ≤ 1 µs,
enabling applications in reinforcement learning, trajectory optimization, and motion planning. …