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Smooth Trajectory Collision Avoidance through Deep Reinforcement Learning
In this work, we proposed several novel agent state and reward function designs to tackle two critical issues in DRL-based navigation solutions: 1) smoothness of the trained flight trajectories; and 2) model generalization to handle unseen environments.
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