A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

Survey of Artificial Intelligence for Card Games and Its Application to the Swiss Game Jass

In the last decades we have witnessed the success of applications of Artificial Intelligence to playing games. In this work we address the challenging field of games with hidden information and card games in particular. Jass is a very popular card game in Switzerland and is closely connected with Swiss culture. To the best of our knowledge, performances of Artificial Intelligence agents in the game of Jass do not outperform top players yet. Our contribution to the community is two-fold. First, we provide an overview of the current state-of-the-art of Artificial Intelligence methods for card games in general. Second, we discuss their application to the use-case of the Swiss card game Jass. This paper aims to be an entry point for both seasoned researchers and new practitioners who want to join in the Jass challenge

ANALYZING HUMAN-INDUCED PATHOLOGY IN THE TRAINING OF REINFORCEMENT LEARNING ALGORITHMS

Modern artificial intelligence (AI) systems trained with reinforcement learning (RL) are increasingly more capable, but agents training to complete tasks in safety critical environments still require millions of trial-and-error training steps. Previous research with a Pong agent has shown that some human heuristics initially accelerate training but cause agent performance to regress to a state of performance collapse. This thesis utilizes the FlappyBird environment to evaluate if the pathology is generalizable. After initially confirming a similar pathology in an unaided agent, comprehensive experimentation was performed with optimizers, weight initialization methods, activation functions, and varied hyperparameters. The pathology persisted across all experiments and the results show the network architecture is likely the principal cause. At a high level, this work illustrates the importance of determining the inherent capacity of an architecture to learn and model complex environments and how more systematic methods to quantify capacity would greatly enhance RL.Outstanding ThesisCaptain, United States Marine CorpsApproved for public release. Distribution is unlimited