  2048 is a single-player stochastic puzzle game. This intriguing and addictive
game has been popular worldwide and has attracted researchers to develop
game-playing programs. Due to its simplicity and complexity, 2048 has become an
interesting and challenging platform for evaluating the effectiveness of
machine learning methods. This dissertation conducts comprehensive research on
reinforcement learning and computer game algorithms for 2048. First, this
dissertation proposes optimistic temporal difference learning, which
significantly improves the quality of learning by employing optimistic
initialization to encourage exploration for 2048. Furthermore, based on this
approach, a state-of-the-art program for 2048 is developed, which achieves the
highest performance among all learning-based programs, namely an average score
of 625377 points and a rate of 72% for reaching 32768-tiles. Second, this
dissertation investigates several techniques related to 2048, including the
n-tuple network ensemble learning, Monte Carlo tree search, and deep
reinforcement learning. These techniques are promising for further improving
the performance of the current state-of-the-art program. Finally, this
dissertation discusses pedagogical applications related to 2048 by proposing
course designs and summarizing the teaching experience. The proposed course
designs adopt 2048-like games as materials for beginners to learn reinforcement
learning and computer game algorithms. The courses have been successfully
applied to graduate-level students and received well by student feedback.
