Variational Autoencoders for exteroceptive perception in reinforcement learning-based collision avoidance

• URL: http://arxiv.org/abs/2404.00623v1
• Date: Sun, 31 Mar 2024 09:25:28 GMT
• Title: Variational Autoencoders for exteroceptive perception in reinforcement learning-based collision avoidance
• Authors: Thomas Nakken Larsen, Eirik Runde Barlaug, Adil Rasheed,
• Abstract summary: Deep Reinforcement Learning (DRL) has emerged as a promising control framework. Current DRL algorithms require disproportionally large computational resources to find near-optimal policies. This paper presents a comprehensive exploration of our proposed approach in maritime control systems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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