Deep reinforcement learning with optimized reward functions for robotic trajectory planning
… trajectory planning. The primary contributions of this paper are summarized as follows: 1)
Considering the features of trajectory planning … of DRL methods in trajectory planning task. 2) …
Considering the features of trajectory planning … of DRL methods in trajectory planning task. 2) …
[HTML][HTML] Deep reinforcement learning based trajectory planning under uncertain constraints
… In this section, we show that DDPG and SAC can learn optimal trajectory planning for
dynamic obstacles collision avoidance. For the evaluation, we compare two different DRL …
dynamic obstacles collision avoidance. For the evaluation, we compare two different DRL …
Multi-agent deep reinforcement learning-based trajectory planning for multi-UAV assisted mobile edge computing
… proposed, where several UAVs having different trajectories fly over the target area and …
reinforcement learning based trajectory control algorithm is proposed for managing the trajectory …
reinforcement learning based trajectory control algorithm is proposed for managing the trajectory …
Reinforcement learning-based collision avoidance and optimal trajectory planning in UAV communication networks
YH Hsu, RH Gau - IEEE Transactions on Mobile Computing, 2020 - ieeexplore.ieee.org
… We adopt reinforcement learning for assisting UAVs to learn collision avoidance without
knowing the trajectories of other UAVs in advance. In addition, for each UAV, we use …
knowing the trajectories of other UAVs in advance. In addition, for each UAV, we use …
Trajectory planning with deep reinforcement learning in high-level action spaces
KR Williams, R Schlossman, D Whitten… - … on Aerospace and …, 2022 - ieeexplore.ieee.org
… This article presents a technique for trajectory planning based on parameterized high-level
actions. These high-level actions are subtrajectories that have variable shape and duration. …
actions. These high-level actions are subtrajectories that have variable shape and duration. …
Trajectory planning for autonomous vehicles using hierarchical reinforcement learning
… Reinforcement Learning (HRL) framework for learning autonomous driving policies. We adapt
a state-ofthe-art algorithm, Hierarchical Double Deep Q-learning (h… waypoint trajectories to …
a state-ofthe-art algorithm, Hierarchical Double Deep Q-learning (h… waypoint trajectories to …
Predictive trajectory planning for autonomous vehicles at intersections using reinforcement learning
… planning policies directly determine how AVs behave. In this paper, we model the trajectory
planning … process (POMDP), and develop a reinforcement learning (RL)-based approach to …
planning … process (POMDP), and develop a reinforcement learning (RL)-based approach to …
UAV trajectory planning in wireless sensor networks for energy consumption minimization by deep reinforcement learning
… 2) We show that the UAV’s trajectory planning problem in the clustered WSN can be
seen as a sequence of decisions. Hence, a sequence-to-sequence pointer networkA* (Ptr-A*) …
seen as a sequence of decisions. Hence, a sequence-to-sequence pointer networkA* (Ptr-A*) …
[HTML][HTML] Intelligent land-vehicle model transfer trajectory planning method based on deep reinforcement learning
L Yu, X Shao, Y Wei, K Zhou - Sensors, 2018 - mdpi.com
… trajectory planning technology is attracting more and more attention and exploration by
researchers at home and abroad. Trajectory planning … trajectory generation in trajectory planning, …
researchers at home and abroad. Trajectory planning … trajectory generation in trajectory planning, …
Combining decision making and trajectory planning for lane changing using deep reinforcement learning
S Li, C Wei, Y Wang - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
… Following decision-making, the trajectory planning stage is entered into, where its output is
… the unified trajectory planning model in Section IV to generate a new trajectory path together …
… the unified trajectory planning model in Section IV to generate a new trajectory path together …
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