{'fraction_non_alphanumeric': 0.03284258210645526, 'fraction_numerical': 0.013590033975084938, 'mean_word_length': 4.815789473684211, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A mathematical model for learning agents on a multi-agent system

A term "the learning agent" on the multi-agent system has appeared in the beginning of 1990's [LJ Lin, 1991]. Since then, a number of researches have been studied some applications of the learning agents on the basis of reinforcement learning such as SLA and Q-learning incorporated with the automation, as well as [Michiko Watanabe et al., 2001]. However, in designing the learning agents, there are questions; what stands on the state and action model, and the state and action evaluate function? How is the reward designed? This paper aims at developing a mathematical model for the learning agents of the multi-agent system to answer these questions. The learning agent is derived from the introduced formulation. Also, the relation of reward between the multi-agents is analyzed using mathematical model as well.