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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Spatial-temporal recurrent reinforcement learning for autonomous ships.

Martin Waltz1, Ostap Okhrin2

  • 1Technische Universität Dresden, Chair of Econometrics and Statistics, esp. in the Transport Sector, Dresden, 01062, Germany.

Neural Networks : the Official Journal of the International Neural Network Society
|June 26, 2023
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Summary
This summary is machine-generated.

This study introduces a novel deep Q-network for autonomous ship steering, enhancing collision avoidance and multi-ship navigation. The approach demonstrates robust performance in complex maritime scenarios.

Keywords:
Autonomous surface vehicleCOLREGDeep reinforcement learningRecurrency

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Area of Science:

  • Artificial Intelligence
  • Robotics
  • Maritime Engineering

Background:

  • Autonomous ship navigation requires advanced decision-making capabilities to handle complex, dynamic maritime environments.
  • Existing methods often struggle with multi-target scenarios and partial observability, limiting their real-world applicability.
  • Ensuring safety through collision avoidance and adherence to maritime regulations is paramount.

Purpose of the Study:

  • To propose a spatial-temporal recurrent neural network architecture for deep Q-networks (DQN) to enhance autonomous ship steering.
  • To develop a robust system capable of managing multiple surrounding ships and partial observability.
  • To integrate a state-of-the-art collision risk metric and International Regulations for Preventing Collisions at Sea (COLREGs) into the agent's decision-making process.

Main Methods:

  • Development of a spatial-temporal recurrent neural network integrated with deep Q-networks.
  • Inclusion of a novel collision risk metric for situation assessment.
  • Incorporation of COLREG rules into the reward function design.
  • Validation using 'Around the Clock' and Imazu (1987) multi-ship encounter datasets.

Main Results:

  • The proposed DQN architecture effectively steers autonomous ships in complex scenarios.
  • The system demonstrates robustness in handling an arbitrary number of target ships and partial observability.
  • Performance comparisons show superiority over artificial potential field and velocity obstacle methods in maritime path planning.
  • The architecture proves compatible with other deep reinforcement learning algorithms, including actor-critic frameworks.

Conclusions:

  • The novel spatial-temporal DQN architecture offers a significant advancement in autonomous ship path planning and collision avoidance.
  • The approach provides a robust and adaptable solution for multi-agent maritime scenarios.
  • This work paves the way for safer and more efficient autonomous maritime operations.