Investigation into the effect of social learning in reinforcement learning board game playing agents

Abstract

This thesis presents the use of social learning to improve the performance of game playing reinforcement learning agents. Agents are placed in a social learning environment as opposed to the Self-Play learning environment. Their performance is monitored and analysed in order to observe how the performance changes compared to Self-Play agents. Two case studies were conducted, one with the game Tic-Tac-Toe and the other with the African board game of Morabaraba. The Tic-Tac-Toe agents used a table based TD ( ) algorithm to learn the Q values. The results from the tests for the Tic-Tac-Toe agents indicate that the social learning agents perform better than the Self-Play agents in both board tests and competitive tests. By increasing the population sizes of the agents the number of superior social agents also increases as well as improvements in their skill level. In the second case study the agents use function approximation and the TD ( ) algorithm because of a larger number of states. The social agents performed better than the Self-Play agents in the board tests and are not superior in the test where they compete against each other. Larger populations were not possible with the Morabaraba agents but the results are still positive as the agents perform well in the board tests.