Position Control and Production of Various Strategies for Deep Learning Go Programs Computer Go programs have exceeded toplevel human players by using deep learning and reinforcement learning techniques. On the other hand, xe2x80x9cEntertainment Go AIxe2x80x9d or xe2x80x9cCoaching Go AIxe2x80x9d are also interesting directions which have not been well investigated. Several researches have been done for entertaining beginners or intermediate players. Position control or producing various strategies are important tasks, and some methods have been proposed and evaluated using a traditional MonteCarlo tree search program. In this paper, we try to adapt the method to LeelaZero, a program based on AlphaGo Zero. There are some critical differences between the previous program and the new program. For example the new program does not use random simulations to the ends of games, then the previous method for producing various strategies cannot be used. In this paper we summarized the differences and some expected problems, and proposed several approaches to solve the problems. It was shown that the modified LeelaZero could play gently against weaker players (48% won against a program Ray). Through experiments using human subjects, it was shown that the average number of unnatural moves per game was 1.22, where that by a simple method without considering naturalness was 2.29. Also we evaluated the proposed method for training xe2x80x9ccenterorientedxe2x80x9d and xe2x80x9cedgecornerorientedxe2x80x9d players, and it was confirmed that human players could identify the produced strategy (center or edgecorner) with a probability of 71.88%.

Deep Active Imitation Learning in FIFA FreeKicks Player Platforms Based on Raw Image and Object Detection State Representations This paper investigates the use of the Deep Active Imitation (DAI) learning strategy in single and multiagents player platforms for FIFA game. DAI is a supervised Deep Learning training strategy in which previously trained agents evolve by mimicking human expertsu0027 behavior through active learning. Such learning strategy has only been validated in singleagent scenarios where the environment is perceived through raw images. FIFA, in spite of being a successful soccer simulation video game representing a very realistic and challenging case study, has been relatively poorly explored in the context of Machine Learning research. Then, the main objective of the present work is to investigate the efficacy of DAI to cope with single and multiagents FIFA platforms in which the environment is represented either by Object Detection Techniques (ODT) or by raw images. The agents were evaluated in terms of the rate of goals obtained in the freekicks scenarios provided by FIFA. The results show that DAI performs well on both the single and multiagent based platforms. Further, the raw image based technique proved to be better than the ODT based method to represent the environment.