Variational Autoencoders for exteroceptive perception in reinforcement learning-based collision avoidance
<|reference_start|>Variational Autoencoders for exteroceptive perception in reinforcement learning-based collision avoidance: Modern control systems are increasingly turning to machine learning algorithms to augment their performance and adaptability. Within this context, Deep Reinforcement Learning (DRL) has emerged as a promising control framework, particularly in the domain of marine transportation. Its potential for autonomous marine applications lies in its ability to seamlessly combine path-following and collision avoidance with an arbitrary number of obstacles. However, current DRL algorithms require disproportionally large computational resources to find near-optimal policies compared to the posed control problem when the searchable parameter space becomes large. To combat this, our work delves into the application of Variational AutoEncoders (VAEs) to acquire a generalized, low-dimensional latent encoding of a high-fidelity range-finding sensor, which serves as the exteroceptive input to a DRL agent. The agent's performance, encompassing path-following and collision avoidance, is systematically tested and evaluated within a stochastic simulation environment, presenting a comprehensive exploration of our proposed approach in maritime control systems.<|reference_end|>
@article{larsen2024variational,
    title={Variational Autoencoders for exteroceptive perception in reinforcement
  learning-based collision avoidance},
    author={Thomas Nakken Larsen, Eirik Runde Barlaug, Adil Rasheed},
    journal={arXiv preprint arXiv:2404.00623},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00623},
    primaryClass={cs.LG cs.RO}
}