A Comparison of the Performance of Neural Q-learning and Soar-RL on a Derivative of the Block Design (BD)/Block Design Multiple Choice (BDMC) Subtests on the WISC-IV Intelligence Test.

摘要

Teaching an autonomous agent to perform tasks that are simple to humans can be complex, especially when the task requires successive steps, has a low likelihood of successful completion with a brute force approach, and when the solution space is too large or too complex to be explicitly encoded. Reinforcement learning algorithms are particularly suited to such situations, and are based on rewards that help the agent to find the optimal action to execute given a certain state. The task investigated in this thesis is a modified form of the Block Design (BD) and Block Design Multiple Choice (BDMC) subtests, used by the Fourth Edition of the Wechsler Intelligence Scale for Children (WISC-IV) to partially assess childrens' learning abilities. This thesis investigates the implementation, training, and performance of two reinforcement learning architectures for this problem: Soar-RL, a production system capable of reinforcement learning, and a Q-learning neural network. The objective is to help define the advantages and disadvantages of solving problems using these architectures. This thesis will show that Soar is intuitive for implementation and is able to find an optimal policy, although it is limited by its execution of exploratory actions. The neural network is also able to find an optimal policy and outperforms Soar, but the convergence of the solution is highly dependent on the architecture of the neural network.