Deep Reinforcement Learning based Local Planner for UAV Obstacle Avoidance using Demonstration Data
This work addresses the data efficiency problem in DRL for UAV obstacle avoidance, which is incremental as it builds upon existing TD3 methods.
The paper tackles UAV navigation in unknown environments by proposing a deep reinforcement learning method that combines imitation learning with reinforcement learning to improve data efficiency, achieving performance in 3D navigation simulations.
In this paper, a deep reinforcement learning (DRL) method is proposed to address the problem of UAV navigation in an unknown environment. However, DRL algorithms are limited by the data efficiency problem as they typically require a huge amount of data before they reach a reasonable performance. To speed up the DRL training process, we developed a novel learning framework which combines imitation learning and reinforcement learning and building upon Twin Delayed DDPG (TD3) algorithm. We newly introduced both policy and Q-value network are learned using the expert demonstration during the imitation phase. To tackle the distribution mismatch problem transfer from imitation to reinforcement learning, both TD-error and decayed imitation loss are used to update the pre-trained network when start interacting with the environment. The performances of the proposed algorithm are demonstrated on the challenging 3D UAV navigation problem using depth cameras and sketched in a variety of simulation environments.