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PathRL: An End-to-End Path Generation Method for Collision Avoidance via Deep Reinforcement Learning
However, two roadblocks arise for training a DRL policy that outputs paths: (1) The action space for potential paths often involves higher dimensions comparing to low-level commands, which increases the difficulties of training; (2) It takes multiple time steps to track a path instead of a single time step, which requires the path to predicate the interactions of the robot w. r. t.
Deep Reinforcement Learning for Localizability-Enhanced Navigation in Dynamic Human Environments
The planner learns to assign different importance to the geometric features and encourages the robot to navigate through areas that are helpful for laser localization.
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