{'fraction_non_alphanumeric': 0.013729977116704805, 'fraction_numerical': 0.0, 'mean_word_length': 5.628787878787879, '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}}
Reinforcement Learning Based on Actions and Opposite Actions

Reinforcement learning is a machine intelligence scheme for learning in highly dynamic and probabilistic environments. The methodology, however, suffers from a major drawback; the convergence to an optimal solution usually requires high computational expense since all states should be visited frequently in order to guarantee a reliable policy. In this paper, a new reinforcement learning algorithm is introduced to achieve a faster convergence by taking into account the opposite actions. By considering the opposite actions simultaneously multiple updates can be made for each state observation. This leads to a shorter exploration period and, hence, expedites the convergence. Experimental results for the grid world problem of different sizes are provided to verify the performance of the proposed approach.