Reinforcement Sailing
نویسندگان
چکیده
This thesis examines applying reinforcement learning to sailing. We give two conceptually different models of a simple sailing boat. Standard tabular reinforcement learning is shown to be ineffective in controlling these naturally continuous models. We examine a method [Smith, 2001b] which adaptively quantises both the state and action spaces and show that it has similar performance to the tabular case but requires only a fraction of the resources. Finally we examine the continuous method of wirefitting [Baird and Klopf, 1993] combined with advantage learning [Baird, 1995]. Our findings suggest that for tasks which require smoothly-changing actions in response to slowly-changing states (such as sailing) the best results are obtained by using a continuous method. Wire-fitting was found to have a slower convergence rate, but might form a good starting point for a yacht’s autopilot.
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