A deep reinforcement learning approach to the problem of golf using an agent limited by human data

Sammanfattning: In the sport of golf, the use of statistics has become prominent as a way of understanding and improving golfers’ golf swings. Even though swing data is easily accessible thanks to a variety of technological tools, it is not always clear how to use this data, especially for amateur golfers. This thesis investigates the feasibility of using reinforcement learning in conjunction with a golfer’s data to play golf, something that could provide insight on how the golfer can improve. Specifically, a Dueling Double Deep Q Network agent and a Multi Pass Deep Q Network agent were trained and evaluated on playing golf from pixel data on two simulated golf courses, using only shot data provided by a real golfer to hit shots. These two reinforcement learning agents were then compared with the golfer on how well they played, in regards to the number of shots hit and distances remaining to the golf holes when the holes were finished. The majority of the results showed no significant difference between either agent and the golfer on both golf courses tested, indicating that the agents could play on a similar level to the golfer. The complexity of the problem caused the agents to have a good knowledge of states that occurred often but poor knowledge otherwise, which is one likely reason why the agents played similarly to but not better than the golfer. Other reasons include lack of training time, as well as potentially non-representative data retrieved from the golfer. It is concluded that there is potential in using reinforcement learning for the problem of golf and possibly for similar problems as well. Moreover, further research could improve the agents such that more valuable insights regarding golfers’ data can be found.