Summary of Real-time Recurrent Learning Using Trace Units in Reinforcement Learning, by Esraa Elelimy et al.
Real-Time Recurrent Learning using Trace Units in Reinforcement Learning
by Esraa Elelimy, Adam White, Michael Bowling, Martha White
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed approach, Recurrent Trace Units (RTUs), is a lightweight modification to linear recurrent architectures (LRUs) that enables efficient training of recurrent neural networks (RNNs) in online reinforcement learning (RL) scenarios. By replacing dense recurrent weights with complex-valued diagonal matrices, RTUs reduce the computational cost while maintaining performance benefits over LRUs when trained using real-time recurrent learning (RTRL). Experimental results show significant outperformance of RTUs compared to other recurrent architectures across various partially observable environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to train Recurrent Neural Networks (RNNs) that helps them learn from experiences in the moment. RNNs are important for many tasks, like playing games or controlling robots. The problem is that training RNNs can be very slow and expensive. To fix this, researchers have been exploring alternative approaches. One idea is to use a special kind of architecture called Linear Recurrent Units (LRUs). These LRUs make training faster by reducing the amount of computation needed. In this paper, scientists propose a new tweak on LRUs that they call Recurrent Trace Units (RTUs). They find that RTUs can learn much faster and better than other approaches while using less energy. |
Keywords
» Artificial intelligence » Reinforcement learning
