TR2022-102

Deep Reinforcement Learning for Optimal Sailing Upwind


    •  Suda, T., Nikovski, D., "Deep Reinforcement Learning for Optimal Sailing Upwind", IEEE International Joint Conference on Neural Networks IJCNN, September 2022.
      BibTeX TR2022-102 PDF
      • @inproceedings{Suda2022sep,
      • author = {Suda, Takumi and Nikovski, Daniel},
      • title = {Deep Reinforcement Learning for Optimal Sailing Upwind},
      • booktitle = {IEEE International Joint Conference on Neural Networks IJCNN},
      • year = 2022,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2022-102}
      • }
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  • Research Areas:

    Artificial Intelligence, Control, Optimization

Abstract:

We describe the application of deep reinforcement learning (DRL) methods to determine the optimal decision policy when sailing a sailboat towards a target point located upwind from the boat’s current position, under the conditions of wind direction and speed that vary according to an unknown stochastic process, as is typical in real sailing races. A model of the dynamics of the sailboat is described together with a suitable choice of actions, in the form of a Markov decision process (MDP), which allows the application of a wide variety of DRL algorithms. Empirical results show that the learned policy outperforms baseline control algorithms that do not take into consideration the variability in wind strength and direction, and instead assume that the current wind conditions will persist indefinitel