{'fraction_non_alphanumeric': 0.03526734926052332, 'fraction_numerical': 0.011376564277588168, 'mean_word_length': 5.769230769230769, '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}}
Scalable Reinforcement Learning via Trajectory Optimization and Approximate Gaussian Process Regression

Over the last decade, reinforcement learning (RL) has begun to be successfully applied to robotics and autonomous systems. While model-free RL has demonstrated promising results [1, 2, 3], it requires human expert demonstrations and relies on lots of direct interactions with the physical systems. In contrast, model-based RL was developed to address the issue of sample inefficiency by learning dynamics models explicitly from data, which helps to provide better generalization [4, 5]. However, model-based methods suffer from two issues: 1) classical value function approximation methods [6, 7] and modern global policy search methods [5] are computationally inefficient for moderate to high-dimensional problems; and 2) model errors significantly degrade the performance.