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Transfer Of Learnt Knowledge With Card Games

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posted on 2023-05-26, 07:26 authored by Livingston, JR
Reinforcement learning algorithms are an important machine learning technique, which can be applied to the process of learning many tasks. Much of the existing work on improving these algorithms, and analysis into the usefulness, only considers agents which have to perform one task. Many real-world applications of reinforcement learning algorithms require that an agent can cope with small variations in their given task, and the application of their learnt knowledge to those tasks. I consider the application of reinforcement learning algorithms to several card games, and the process of transferring learnt knowledge between these card games. The two card games used, Cut-Throat Euchre and Sergent-Major, are similar in their rules and the strategies that are used to play the game. The differences between the two games are used to measure the effectiveness of transferring knowledge between them, using a common-state approach. These simulations of playing card-games indicate that the tasks of playing these two games are similar enough that knowledge can effectively be shared between the two. An improvement in the ability of an agent to play one of the games, results in a significant improvement in the ability of the agent to play the other game.

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