Abstract
Reinforcement learning has quickly risen in popularity because of its simple, intuitive nature, and its 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 starting conditions on the performance of a reinforcement learning agent.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mnih V, Kavukcuoglu K, Silver D, Graves A, Antonoglou I, Wierstra D, Riedmiller M (2013) Playing atari with deep reinforcement learning
Silver D, Hubert T, Schrittwieser J, Antonoglou I, Lai M, Guez A, Lan-tot M, Sifre L, Kumaran D, Graepel T, Lillicrap T, Simonyan K, Hassabis D (2017) Mastering chess and shogi by self-play with a general reinforcement learning algorithm
Wang Z et al (2018) Deep reinforcement learning with knowledge transfer for online rides order dispatching. In: 2018 IEEE International Conference on Data Mining (ICDM). IEEE
Manfredi V et al (2021) Relational deep reinforcement learning for routing in wireless networks. In: 2021 IEEE 22nd international symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM). IEEE
Lample G, Chaplot DS (2021) Playing FPS games with deep rein-forcement learning. In: Thirty-first AAAI conference on artificial intelligence
Zhou Z, Li X, Zare RN (2017) Optimizing chemical reactions with deep reinforcement learning. ACS Cent Sci 3(12):1337–1344
Baldazo D, Parras J, Zazo S (2019) Decentralized multi-agent deep reinforce-ment learning in swarms of drones for flood monitoring. In: 2019 27th European Signal Processing Conference (EUSIPCO). IEEE
Landajuela M et al (2021) Discovering symbolic policies with deep reinforcement learning. In: International conference on machine learning, PMLR
Zhao R et al (2020) Deep reinforcement learning based mobile edge computing for intelligent Internet of Things. Phys Commun 43:101184
Kiran BR et al (2021) Deep reinforcement learning for autonomous driving: a survey. IEEE Trans Intelligent Transp Syst
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Angue-lov D, Erhan D, Van-houcke V, Rabinovich A (2015) Going deeper with convolutions
Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G, Petersen S, Beattie C, Sadik A, Antonoglou I, King H, Kumaran D, Wierstra D, Legg S, Hassabis D (2014) Human-level control through deep reinforcement learning
Yin H, Pan SJ (2017) Knowledge transfer for deep reinforcement learning with hierarchical experience replay
Mnih V, Badia AP, Mirza M, Graves A, Harley T, Lillicrap TP, Silver D, Kavukcuoglu K (2016) Asynchronous methods for reinforcement learning
Even-Dar E, Mannor S, Mansour Y (2006) Action elimination and stopping conditions for reinforcement learning
Lin Y-L, Hong Z-W, Liao Y-H, Shih M, Liu M, Sun M (2017) Tactics of adversarial attack on deep reinforcement learning agents
Pinto L, Davidson J, Sukthankar R, Gupta A (2017) Robust adversarial reinforcement learning
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Islam, T., Abid, D.M.H., Rahman, T., Zaman, Z., Mia, K., Hossain, R. (2023). Transfer Learning in Deep Reinforcement Learning. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Seventh International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 447. Springer, Singapore. https://doi.org/10.1007/978-981-19-1607-6_13
Download citation
DOI: https://doi.org/10.1007/978-981-19-1607-6_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-1606-9
Online ISBN: 978-981-19-1607-6
eBook Packages: EngineeringEngineering (R0)