Transfer Learning in Deep Reinforcement Learning

dc.contributor.author	Islam, Tariqul
dc.contributor.author	Abid, Dm. Mehedi Hasan
dc.contributor.author	Rahman, Tanvir
dc.contributor.author	Zaman, Zahura
dc.date.accessioned	2024-06-12T03:52:48Z
dc.date.available	2024-06-12T03:52:48Z
dc.date.issued	2022-08
dc.identifier.uri	http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/12703
dc.description.abstract	Reinforcement learning has quickly risen in popularity because of its simple, intuitive nature and it's powerful results. In this paper, we study a number of reinforcement learning algorithms, ranging from asynchronous q-learning to deep reinforcement learning. We focus on the improvements they provide over standard reinforcement learning algorithms, as well as the impact of initial start-ing conditions on the performance of a reinforcement learning agent	en_US
dc.language.iso	en_US	en_US
dc.publisher	Daffodil International University	en_US
dc.subject	Deep Learning	en_US
dc.subject	Transfer Learning	en_US
dc.subject	Reinforcement Learning	en_US
dc.subject	Con-volutional Neural Networks	en_US
dc.subject	Q-networks	en_US
dc.title	Transfer Learning in Deep Reinforcement Learning	en_US
dc.type	Article	en_US