Using Deep Reinforcement Learning for Navigation in Simulated Hallways

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2023

Autores

Leao, G (Autor) Outra; Almeida, F (Autor) Outra; Trigo, E (Autor) Outra; Ferreira, H (Autor) Outra; Armando Jorge Sousa (Autor) FEUP; reis, lp (Autor) FEUP

Ata de Conferência Internacional

Título: 2023 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS, ICARSC

Páginas: 207-213

IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

Tomar, PORTUGAL, APR 26-27, 2023

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-00Y-MGS

DOI: 10.1109/icarsc58346.2023.10129605

Abstract (EN): Reinforcement Learning (RL) is a well-suited paradigm to train robots since it does not require any previous information or database to train an agent. This paper explores using Deep Reinforcement Learning (DRL) to train a robot to navigate in maps containing different sorts of obstacles and which emulate hallways. Training and testing were performed using the Flatland 2D simulator and a Deep Q-Network (DQN) provided by OpenAI gym. Different sets of maps were used for training and testing. The experiments illustrate how well the robot is able to navigate in maps distinct from the ones used for training by learning new behaviours (namely following walls) and highlight the key challenges when solving this task using DRL, including the appropriate definition of the state space and reward function, as well as of the stopping criteria during training.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 7