mlsanityData to 14 Oct 2026

Exploration in Knowledge Transfer Utilizing Reinforcement Learning

arXiv 2407.10835 ↗First posted 15 Jul 2024cs.LG cs.AIcited by 0

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

The contribution focuses on the problem of exploration within the task of knowledge transfer. Knowledge transfer refers to the useful application of the knowledge gained while learning the source task in the target task. The intended benefit of knowledge transfer is to speed up the learning process of the target task. The article aims to compare several exploration methods used within a deep transfer learning algorithm, particularly Deep Target Transfer Q-learning. The methods used are ε-greedy, Boltzmann, and upper confidence bound exploration. The aforementioned transfer learning algorithms and exploration methods were tested on the virtual drone problem. The results have shown that the upper confidence bound algorithm performs the best out of these options. Its sustainability to other applications is to be checked.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 7 references that are in the map. First posted on arXiv 15 Jul 2024.

Builds on

7 of 7 references.

References · 7

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2016Successor Features for Transfer in Reinforcement Learningcited by 340
  3. 2022Exploration in Deep Reinforcement Learning: A Surveycited by 166
  4. 2021A Survey of Exploration Methods in Reinforcement Learningcited by 73
  5. 2019Autonomous Navigation via Deep Reinforcement Learning for Resource Constraint Edge Nodes using Transfer Learningcited by 19
  6. 2020REPAINT: Knowledge Transfer in Deep Reinforcement Learningcited by 14
  7. 2018Target Transfer Q-Learning and Its Convergence Analysiscited by 6

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  1. 2024Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning
  2. 2024Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic Review
  3. 2023Foundations for Transfer in Reinforcement Learning: A Taxonomy of Knowledge Modalities
  4. 2025Knowledge Transfer in Model-Based Reinforcement Learning Agents for Efficient Multi-Task Learning
  5. 2024SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning
  6. 2024Reinforcement Learning with a Focus on Adjusting Policies to Reach Targets
  7. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey
  8. 2025Deep Transfer Q-Learning for Offline Non-Stationary Reinforcement Learning