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REINFORCE MENTLEARNING TAMING AN AUTO NOMOUS SURFACE VEHICLE FOR PATH FOLLOWING AND COLLISION AVOID AN CEUSING DEEP

REINFORCE MENTLEARNING TAMING AN AUTO NOMOUS SURFACE VEHICLE FOR PATH FOLLOWING AND COLLISION AVOID AN CEUSING DEEP

Abstract:

In this article, we explore the feasibility of applying proximal policy optimization, a state-ofthe-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 AI 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 realtime 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 close to 100%.

SOFTWARE AND HARDWARE REQUIREMENTS:

HARDWARE SPECIFICATIONS:

Processor: I3/Intel

Processor RAM: 4GB (min)

Hard Disk: 128 GB

Key Board: Standard Windows Keyboard

Mouse: Two or Three Button Mouse

Monitor: Any

SOFTWARE SPECIFICATIONS:

Operating System: Windows 7+

Server-side Script: Python 3.6+

IDE: PyCharm

Libraries Used: Pandas, Numpy,Flask

March 15, 2022

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