Publication Details

SELECT * FROM publications WHERE Record_Number=11166
Reference TypeThesis
Author(s)Tschirner, J.
Year2018
TitleBoosted Deep Q-Network
Journal/Conference/Book TitleBachelor Thesis
AbstractDeep reinforcement learning achieved outstanding results in the last years, since it has emerged from reinforcement learning combined with the capabilities of deep neural networks. This thesis is about Boosted Deep Q-Network (BDQN), a novel deep reinforcement learning algorithm that employs gradient boosting to estimate the action-value function in reinforcement learning problems. The aim with this approach is to meet some of the major challenges still present in the field. On the one hand, they consist of the necessary model complexity for high-dimensional problems and moreover, of the requirement to make efficiently use of the environment’s data. BDQN was evaluated empirically first on two standard control problems, and then on one more complex environment with a high-dimensional observation space. In comparison to Deep Q-Network (DQN), the algorithm performed competitively on the standard tasks using a smaller model complexity, but had serious problems learning a reasonable policy in the complex environment.
URL(s) /uploads/Team/SamueleTosatto/bachelor_thesis_jeremy_tschirner.pdf
Link to PDF/uploads/Team/SamueleTosatto/bachelor_thesis_jeremy_tschirner.pdf
LanguageEnglish

  

zum Seitenanfang