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

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Duarte, FF (Author) Other; lau, n (Author) Other; Pereira, A (Author) Other; reis, lp (Author) FEUP

Conference proceedings International

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

Pages: 263-275

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

Porto, PORTUGAL, NOV 20-22, 2019

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 1 Citation

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13