Journal
JOURNAL OF MARINE SCIENCE AND ENGINEERING
Volume 10, Issue 10, Pages -Publisher
MDPI
DOI: 10.3390/jmse10101406
Keywords
autonomous underwater vehicle; dynamic target tracking; deep reinforcement learning; experience replay technique
Categories
Funding
- Key Projects of Science and Technology Plan of Zhejiang Province [2019C04018]
- Opening Research Fund of National Engineering Laboratory for Test and Experiment Technology of Marine Engineering Equipment [750NEL-2021-02]
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This research proposes a model-free reinforcement learning algorithm for target tracking of autonomous underwater vehicles (AUVs). By utilizing the actor-critic framework and experience replay technique, the algorithm achieves quick and stable learning of target tracking tasks in various motion states, improving control performance.
Due to the unknown motion model and the complexity of the environment, the problem of target tracking for autonomous underwater vehicles (AUVs) became one of the major difficulties in model-based controllers. Therefore, the target tracking task of AUV is modeled as a Markov decision process (MDP) with unknown state transition probabilities. Based on actor-critic framework and experience replay technique, a model-free reinforcement learning algorithm is proposed to realize the dynamic target tracking of AUVs. In order to improve the performance of the algorithm, an adaptive experience replay scheme is further proposed. Specifically, the proposed algorithm utilizes the experience replay buffer to store and disrupt the samples, so that the time series samples can be used for training the neural network. Then, the sample priority is arranged according to the temporal difference error, while the adaptive parameters are introduced in the sample priority calculation, thus improving the experience replay rules. The results confirm the quick and stable learning of the proposed algorithm, when tracking the dynamic targets in various motion states. Additionally, the results also demonstrate good control performance regarding both stability and computational complexity, thus indicating the effectiveness of the proposed algorithm in target tracking tasks.
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