Object-model transfer in the general video game domain

Abstract

Reinforcement learning agents often benefit from learning models that predict their environment. However, learned models may not generalize well to novel situations. This thesis investigates the potential for a transfer learning approach to address the challenge in the video game domain. The approach helps agents learn models of new games by transferring knowledge from previously learned games. Transfer is facilitated by decomposing games into the objects they contain. The assumption is that it is easier to relate features between objects from different games than features between whole environments of different games. Experiments show that predictions made by this method are more accurate than predictions made without transferred knowledge, and this improvement is demonstrated to result in increased efficiency in a task where an agent explores a maze-like game. The conclusion is that model learning can be enhanced by transferring object models from previously learned environments.