Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning

2019-12-18 · Eivind Meyer, Haakon Robinson, Adil Rasheed, Omer San

In this article, we explore the feasibility of applying proximal policy optimization, a state-of-the-art deep reinforcement learning algorithm for continuous control tasks, on the dual-objective problem of controlling an underactuated autonomous surface vehicle to follow an a priori known path while avoiding collisions with non-moving obstacles along the way. The artificial intelligent agent, which is equipped with multiple rangefinder sensors for obstacle detection, is trained and evaluated in a challenging, stochastically generated simulation environment based on the OpenAI gym python toolkit. Notably, the agent is provided with real-time insight into its own reward function, allowing it to dynamically adapt its guidance strategy. Depending on its strategy, which ranges from radical path-adherence to radical obstacle avoidance, the trained agent achieves an episodic success rate between 84 and 100%.

📄 PDF Abstract BibTeX arXiv:1912.08578

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Collision Avoidance continuous-control Continuous ControlDeep Reinforcement LearningOpenAI Gym reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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