… Aiming at trajectory planning for autonomous driving at dynamic highway … trajectory for
learning and trajectory planning, but set T to 30. It means that the first 3 seconds of the trajectories …

… environment consists of a feasible trajectory generator module, a … reinforcement learning
environment, which also includes a classic control loop. The inputs of the trajectory planning …

… if model-free deep reinforcement learning combined with trajectory optimization can perform
… the art learning from demonstration can outperform model-free reinforcement learning from …

… based on perceptual information, including historical states and trajectories, directly into
reinforcement learning algorithms during the study of autonomous driving decision planning. …

… of human driven trajectories with optimal policies of our planner under learned … trajectory
planner in highly automated vehicles must be able to generate comfortable and safe trajectories …

… Kernel-based API in [8] and ELM-API in [9] dealt with simple overtaking tasks and lane-changing
issues without considering the trajectory planning when there exists the obstacles. In [10…

… , and trajectory planning algorithms in traditional CNC and can smooth the trajectory effectively
to … The state and reward functions designed and optimized for reinforcement learning are …

… trajectory planning for multi-vehicle parking problem and proposes a safe and efficient trajectory
planning … In this paper, we formulate the parking trajectory planning of multiple vehicles …

… The technique used in this work is based on a Multi-Agent Deep Deterministic Policy
Gradient (MADDPG), which belongs to the field of multi-agent reinforcement learning. The …

… The key to the trajectory planning method for airborne radar in this paper lies in the training
of the deep reinforcement learning agent, with the most crucial aspect of training being the …