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- W4320038068 abstract "This paper proposes an end-to-end quadrotor path planning framework derived from deep reinforcement learning (DRL), which is sensor-based with Deep Deterministic Policy Gradient (DDPG) algorithm and search-based planner optimization. We use the current excellent search-based algorithm to demonstrate the path planning of the quadrotor to reach the goal and prioritize it into an experience buffer for replay. Semi-physical simulation experiments show that our framework can make the learning process converge quickly and realize the efficient use of information for exploration in the unknown environment, effectively improving the ability compare with the original DDPG algorithm in complex tasks." @default.
- W4320038068 created "2023-02-12" @default.
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- W4320038068 date "2023-01-01" @default.
- W4320038068 modified "2023-10-01" @default.
- W4320038068 title "A Deep Reinforcement Learning Approach for Quadrotor Path Planning with Search-Based Planner Optimization" @default.
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- W4320038068 doi "https://doi.org/10.1007/978-981-19-6613-2_606" @default.
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