Summary of Prend: Enhancing Intrinsic Motivation in Reinforcement Learning Through Pre-trained Network Distillation, by Mohammadamin Davoodabadi et al.
PreND: Enhancing Intrinsic Motivation in Reinforcement Learning through Pre-trained Network Distillation
by Mohammadamin Davoodabadi, Negin Hashemi Dijujin, Mahdieh Soleymani Baghshah
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to enhancing intrinsic motivation in reinforcement learning (RL) is introduced, building upon the widely used Random Network Distillation (RND) method. The proposed Pre-trained Network Distillation (PreND) addresses limitations of RND by incorporating pre-trained representation models into both target and predictor networks, resulting in more meaningful and stable intrinsic rewards. This leads to better exploration and improved overall performance and sample efficiency on Atari domain tasks. By controlling the learning rate, simple yet effective variants of the predictor network optimization are explored. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists are trying to make computer programs learn new things by themselves, without needing lots of rewards or punishments. They’re using a method called reinforcement learning (RL) and want to make it better. They’ve created a new way called PreND that helps the program learn more effectively by giving it a better understanding of what’s important. This makes the program explore its environment in a smarter way, which leads to better results. |
Keywords
» Artificial intelligence » Distillation » Optimization » Reinforcement learning
