{'fraction_non_alphanumeric': 0.011137629276054098, 'fraction_numerical': 0.0, 'mean_word_length': 5.0772946859903385, '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}}
Adaptive Multiagent Q-Learning with Initial Heuristic Approximation

The problem of effective coordination learning of multiple autonomous agents in a multiagent system (MAS) is one of the most complex challenges in artificial intelligence because of two principal cumbers: non-stationarity of the environment and exponential growth of its dimensionality with number of agents. Non-stationarity of the environment is due to the dependence of the transition function of one agent on the decisions of other agents. Dimensionality of the problem is exponential in number of agents since each state of the system is a joint state of all agents and each action is a joint action composed of actions of each agent. In this work we address these two problems for the restricted class of environments known as the coordination stochastic games. To do that, we use the adaptive play technique to cope with nonstationarity of the environment and a heuristic approximation of the agents’ initial preferences to restrict the state space of multiagent learning. We tested our approach on a set of different configurations of the multiagent grid world problem and the results show that in many cases there is a significant reduction of complexity of the learning process.