Evaluation Function Approximation for Scrabble
  The current state-of-the-art Scrabble agents are not learning-based but
depend on truncated Monte Carlo simulations and the quality of such agents is
contingent upon the time available for running the simulations. This thesis
takes steps towards building a learning-based Scrabble agent using self-play.
Specifically, we try to find a better function approximation for the static
evaluation function used in Scrabble which determines the move goodness at a
given board configuration. In this work, we experimented with evolutionary
algorithms and Bayesian Optimization to learn the weights for an approximate
feature-based evaluation function. However, these optimization methods were not
quite effective, which lead us to explore the given problem from an Imitation
Learning point of view. We also tried to imitate the ranking of moves produced
by the Quackle simulation agent using supervised learning with a neural network
function approximator which takes the raw representation of the Scrabble board
as the input instead of using only a fixed number of handcrafted features.
