Benchmarking Deep and Non-deep Reinforcement Learning Algorithms for Discrete Environments

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2020

Autores

Duarte, FF (Autor) Outra; lau, n (Autor) Outra; Pereira, A (Autor) Outra; reis, lp (Autor) FEUP

Ata de Conferência Internacional

Título: FOURTH IBERIAN ROBOTICS CONFERENCE: ADVANCES IN ROBOTICS, ROBOT 2019, VOL 2

Páginas: 263-275

4th Iberian Robotics Conference (Robot) - Advances in Robotics

Porto, PORTUGAL, NOV 20-22, 2019

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00R-QNN

DOI: 10.1007/978-3-030-36150-1_22

Abstract (EN): Given the plethora of Reinforcement Learning algorithms available in the literature, it can prove challenging to decide on the most appropriate one to use in order to solve a given Reinforcement Learning task. This work presents a benchmark study on the performance of several Reinforcement Learning algorithms for discrete learning environments. The study includes several deep as well as non-deep learning algorithms, with special focus on the Deep Q-Network algorithm and its variants. Neural Fitted Q-Iteration, the predecessor of Deep Q-Network as well as Vanilla Policy Gradient and a planner were also included in this assessment in order to provide a wider range of comparison between different approaches and paradigms. Three learning environments were used in order to carry out the tests, including a 2D maze and two OpenAI Gym environments, namely a custom-built Foraging/Tagging environment and the CartPole environment.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13