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Aprendizado por Reforço aplicado à navegação 3D: Uma Abordagem baseada em Processos Decisórios de Markov

Year: 2024

Type: conference-paper

Abstract: This paper explores the application of Markov Decision Processes (MDPs) in Reinforcement Learning to find a navigation policy in three-dimensional (3D) spaces. MDPs can define a comprehensive state space and guide the selection of optimal navigation strategies. The proposed methodology included the definition of the modeled environment, where each point was represented by a viable state, and the identification of possible actions, including movements in different three-dimensional directions. Furthermore, a reward function was designed that penalizes longer trajectories and encourages efficient collision avoidance. The results showed that the models were able to find a policy that allows the robot to execute the shortest path, both in the absence and presence of obstacles.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17064113

Authors: Sousa, Leandro Henrique Vidigal, de Rezende Freitas, Elias José, Neto, Armando A., Pimenta, Luciano C. A.

Institutions: Instituto Federal de Educação, Ciência e Tecnologia de Minas Gerais, Universidade Federal de Minas Gerais

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7080128560

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