Cited by 528Reinforcement Learning in RoboticsAdvanced Bandit Algorithms ResearchArtificial Intelligence in Games

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Exploration in deep reinforcement learning: A survey

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Ladosz, Pawel, Weng, Lilian, Kim, Minwoo, Oh, Hyondong·2022·Information Fusion

DOI 10.1016/j.inffus.2022.03.003

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“Currently, imitation learning is an effective method for this [1], but it requires high-quality demonstrations in different situations and may lead to overfitting, causing agents to only imitate expert behavior without adapting to new environments or tasks.”
Method
State-of-the-Art of Deep Learning in Multidisciplinary Optical Coherence Tomography Applications
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“To address the dynamic events, reinforcement learning (RL) [14] is an ideal proposal.”
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Dong, B., Huang, L., Pang, N., Chen, H., & Zhang, W. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3481887 · Cited by 10
“While RL algorithms have undergone extensive investigation in both theoretical and practical aspects [13], [14], [15], the exploration–exploitation dilemma impedes the performance improvement of RL algorithms [16].”
Metaverse for logistics and supply chain management: new framework and research advances
Zhang, Z., Wu, H., Qiu, Y., Liao, Z., & Song, M. (2025) · The International Journal of Logistics Management · doi:10.1108/ijlm-07-2024-0461 · Cited by 12
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“Struggling with the burdensome design of external rewards and the scarcity of extrinsic rewards poses an urgent challenge for previous DRL methods [11], [12], [13], [14], [15], [16], [17], [18].”
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“Deep Reinforcement Learning (DRL) obtains relevance by learning patterns and extracting rules from massive data, without manual feature extraction [7].”
Background
Reinforcement learning path planning method incorporating multi-step Hindsight Experience Replay for lightweight robots
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“The reward sparsity problem refers to the situation where the reward signal appears only in a few states or actions, making it difﬁcult for the agent to learn the correct behavioral policy [31].”
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Background
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“The ultimate goal is for the agent to be trained to acquire an optimal strategy that maximizes cumulative rewards [19].”
Background
VCSAP: Online reinforcement learning exploration method based on visitation count of state-action pairs
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“Restrictions apply. nonconvex optimization problems in dynamic wireless communication environments [18], [19].”
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“119 For example, mimicking the learning and growing of children, curiosity is a novel idea for designing exploration strategy.”
An Adaptive Energy Orchestrator for Cyberphysical Systems Using Multiagent Reinforcement Learning
Robles-Enciso, A., Robles-Enciso, R., & Skármeta, A. (2024) · Smart Cities · doi:10.3390/smartcities7060125 · Cited by 7
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“Deepreinforcementlearning(DRL)isahybriddeeplearn-ing and reinforcement learning method [13–15].”
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“The existing RL-based MKGR method is limited by manual rewards and sparse relations [1], [20].”
Method
Bridging BioSciences and technology: The impact of AI & GenAI in life sciences and agribusiness
da Cunha, N. B., Fernandes, F. C., Gil-Ley, A., Franco, O. L., Timakondu, N., & Costa, F. F. (2025) · Gene · doi:10.1016/j.gene.2025.149623 · Cited by 7
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“In robotics, random exploration is a common mechanism used in learning from reinforcement signals [1], [2], navigation [3], and manipulation and planning [4].”
Background
An Integrated DQN and RF Packet Routing Framework for the V2X Network
Yen, C.-E., Jhang, Y.-S., Hsieh, Y.-H., Chen, Y., Kuo, C., & Chang, I.-C. (2024) · Electronics · doi:10.3390/electronics13112099 · Cited by 6
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“These problems are exacerbated when considering RL for control and robotics, where objectives are often naturally expressed with binary success and failure outcomes and interactions with the environment are limited in rate and number [2, 4].”
Dynamic Pricing Strategy for Data Product Through Deep Reinforcement Learning
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Overhead line path planning based on deep reinforcement learning and geographical information system
Chen, J., Yang, Y. M., Peng, L., Yang, L., Han, Y., & Ge, X. (2025) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2025.110468 · Cited by 6
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“Value-based meth-ods seek the optimal value function for each state, expressed as ( v ∗ ( s ) = max a q ∗ ( s, a ) [38].”
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“Then, the agent can select the new action a t + 1 based on the current state s t + 1 [18].”
Background
Surfing Information: The Challenge of Intelligent Decision-Making
Wu, C., & Zhang, Z. (2023) · Intelligent Computing · doi:10.34133/icomputing.0041 · Cited by 6
Multi-agent reinforcement learning with weak ties
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“As described in [20,21], reinforcement learning is a type of machine learning that aims to maximize the outcome of a given system by using a dynamic and autonomous trial-and-error approach.”
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Fan, K., & Chen, Z. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3559826 · Cited by 5
“Safe Exploration: The core concept behind safe exploration is to ensure the agent explores only within a safety-constrained subset of Markov Decision Processes (MDPs) [11], [12].”
Diversity-enhanced conversational recommendation via multi-agent reinforcement learning
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“Reinforcement learning (RL) is naturally suited to tackle such problems [10, 11].”
Development of deep learning models for high-resolution exposome mapping and health impact assessment
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“Despite these advances, challenges remain in the application of deep learning to exposome mapping (26).”
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AUV Path Planning in Complex Ocean Environment via Deep Reinforcement Learning Assisted by Edge Computing
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“In deep reinforcement learning, the neural network is used to ﬁt each function of reinforcement learning, and then the network is trained by stochastic gradient descent algorithm to make the network approximate to the real function [30].”
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Learning to Regrasp Using Visual–Tactile Representation-Based Reinforcement Learning
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Authors: Ladosz, P., Weng, L., Kim, M., & Oh, H.
Affiliations: OpenAI (United States) (US)
Ulsan National Institute of Science and Technology (KR)
Corresponding author: Oh, Hyondong
ORCID: Kim, Minwoo
Oh, Hyondong
Venue: Information Fusion
Publisher: Elsevier BV
Published: 2022-09
Volume: 85
Pages: 1-22
ISSN: 1566-2535 (ISSN-L)
DOI: 10.1016/j.inffus.2022.03.003
OpenAlex: W4220747123
Cited by: 525
References: 185
Citations by year: 2026: 112 · 2025: 198 · 2024: 149 · 2023: 59 · 2022: 7
FWCI: 33.97
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Information Fusion (publishedVersion)
arXiv (Cornell University) (submittedVersion)
Research Explorer (The University of Manchester) (submittedVersion, cc-by-nc-nd)
Scholarworks@UNIST (Ulsan National Institute of Science and Technology) (submittedVersion)
arXiv (Cornell University)
Funders: Ministry of Science, ICT and Future Planning (2020M3C1C1A01082375, 2020R1F1A1049066)
National Research Foundation of Korea
Ministry of Education (2020R1A6A1A03040570)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
Keywords: safe exploration, random exploration, reward learning

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