ReCom: A deep reinforcement learning approach for semi-supervised tabular data labeling

Zaks, Guy; Katz, Gilad

doi:10.1016/j.ins.2021.12.076

Cited by 8 publications

(1 citation statement)

References 22 publications

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“…However, one limitation of many of these methods is that they often require manual adjustment of parameters, which can make them less effective and limit their ability to generalize well. Additionally, while RLMDP offers resource efficiency advantages, it struggles to efficiently utilize all empirical data [27][28][29]. To overcome this limitation, the Prioritized Experience Replay (PER) sampling technique has been introduced and integrated with DQN and DDQN methods, resulting in DQN-PER and DDQN-PER approaches [8,9,30].…”

Section: Introductionmentioning

confidence: 99%

A Reinforcement Learning Method of Solving Markov Decision Processes: An Adaptive Exploration Model Based on Temporal Difference Error

Wang,

Yang,

Chen

et al. 2023

Electronics

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Traditional backward recursion methods face a fundamental challenge in solving Markov Decision Processes (MDP), where there exists a contradiction between the need for knowledge of optimal expected payoffs and the inability to acquire such knowledge during the decision-making process. To address this challenge and strike a reasonable balance between exploration and exploitation in the decision process, this paper proposes a novel model known as Temporal Error-based Adaptive Exploration (TEAE). Leveraging reinforcement learning techniques, TEAE overcomes the limitations of traditional MDP solving methods. TEAE exhibits dynamic adjustment of exploration probabilities based on the agent’s performance, on the one hand. On the other hand, TEAE approximates the optimal expected payoff function for subprocesses after specific states and times by integrating deep convolutional neural networks to minimize the temporal difference error between the dual networks. Furthermore, the paper extends TEAE to DQN-PER and DDQN-PER methods, resulting in DQN-PER-TEAE and DDQN-PER-TEAE variants, which not only demonstrate the generality and compatibility of the TEAE model with existing reinforcement learning techniques but also validate the practicality and applicability of the proposed approach in a broader MDP reinforcement learning context. To further validate the effectiveness of TEAE, the paper conducts a comprehensive evaluation using multiple metrics, compares its performance with other MDP reinforcement learning methods, and conducts case studies. Ultimately, simulation results and case analyses consistently indicate that TEAE exhibits higher efficiency, highlighting its potential in driving advancements in the field.

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