Reinforcement imitation learning for reliable and efficient autonomous navigation in complex environments

• Published in: Neural computing & applications Vol. 36; no. 20; pp. 11945 - 11961
• Main Author: Kumar, Dharmendra
• Format: Journal Article
• Language: English
• Published: London Springer London 01.07.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Autonomous navigation; Collision avoidance; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Machine learning; Moving obstacles; Original Article; Probability and Statistics in Computer Science; Unmanned aerial vehicles; Q-learning; Deep neural networks; Imitation learning; Autonomous navigation; Reinforcement learning; Dynamic environments
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-024-09678-y

Reinforcement learning (RL) and imitation learning (IL) are quite two useful machine learning techniques that were shown to be potential in enhancing navigation performance. Basically, both of these methods try to find a policy decision function in a reinforcement learning fashion or through imitation. In this paper, we propose a novel algorithm named Reinforcement Imitation Learning (RIL) that naturally combines RL and IL together in accelerating more reliable and efficient navigation in dynamic environments. RIL is a hybrid approach that utilizes RL for policy optimization and IL as some kind of learning from expert demonstrations with the inclusion of guidance. We present the comparison of the convergence of RIL with conventional RL and IL to provide the support for our algorithm’s performance in a dynamic environment with moving obstacles. The results of the testing indicate that the RIL algorithm has better collision avoidance and navigation efficiency than traditional methods. The proposed RIL algorithm has broad application prospects in many specific areas such as an autonomous driving, unmanned aerial vehicles, and robots.