{'fraction_non_alphanumeric': 0.010261194029850746, 'fraction_numerical': 0.0009328358208955224, 'mean_word_length': 5.062146892655368, '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}}
Crowd Navigation in an Unknown and Dynamic Environment Based on Deep Reinforcement Learning

This paper presents an approach for solving the crowd navigation problem in an unknown and dynamic environment based on deep reinforcement learning. In our approach, we first make four leader agents learn how to reach their goals and avoid collisions with static and dynamic obstacles in an unknown environment by use of proximal policy optimization combined with Long short-term memory and a collision prediction algorithm. In the second stage, we make each leader agent arrive at a specific goal several times and record its trajectory as the guiding path so that the members in its group know how to reach their goals. We adopt the Reciprocal Velocity Obstacle algorithm to make agents not collide with others. Finally, we simulate the scenario of four groups moving towards their goals simultaneously using the Unity 3D engine. The experimental results demonstrate self-learning ability of a crowd who can reach their goals successfully in an unknown and dynamic environment.