# Exploration in Knowledge Transfer Utilizing Reinforcement Learning

arXiv: https://arxiv.org/abs/2407.10835 · HTML: https://mlsanity.com/paper/2407.10835/

## 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.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 7 of 7)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Successor Features for Transfer in Reinforcement Learning](https://mlsanity.com/paper/1606.05312/) (2016)
- [Exploration in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2205.00824/) (2022)
- [A Survey of Exploration Methods in Reinforcement Learning](https://mlsanity.com/paper/2109.00157/) (2021)
- [Autonomous Navigation via Deep Reinforcement Learning for Resource Constraint Edge Nodes using Transfer Learning](https://mlsanity.com/paper/1910.05547/) (2019)
- [REPAINT: Knowledge Transfer in Deep Reinforcement Learning](https://mlsanity.com/paper/2011.11827/) (2020)
- [Target Transfer Q-Learning and Its Convergence Analysis](https://mlsanity.com/paper/1809.08923/) (2018)

## 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.