Complexity and problem solving : A tale of two systems

Abstract: The purpose of this thesis is to investigate if increasing complexity for a problem makes a difference for a learning system with dual parts. The dual parts of the learning system are modelled after the Actor and Critic parts from the Actor-Critic algorithm, using the reinforcement learning framework. The results conclude that not any difference can be found in the relative performance in the Actor and Critic parts when increasing the complexity of a problem. These results could depend on technical difficulties in comparing the environments and the algorithms. The difference in complexity would then be non-uniform in an unknowable way and uncertain to use as comparison. If on the other hand the change of complexity is uniform, this could point to the fact that there is an actual difference in how each of the actor and critic handles different types of complexity. Further studies with a controlled increase in complexity are needed to establish which of the scenarios is most likely to be true. In the discussion an idea is presented of using the Actor-Critic framework as a model to understand the success rate of psychological treatments better.