Summary of K-percent Evaluation For Lifelong Rl, by Golnaz Mesbahi et al.
K-percent Evaluation for Lifelong RL
by Golnaz Mesbahi, Parham Mohammad Panahi, Olya Mastikhina, Martha White, Adam White
First submitted to arxiv on: 2 Apr 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 is proposed for evaluating lifelong reinforcement learning (RL) agents in this paper, which addresses the challenge of limited access to the environment. The traditional practice of assuming unfettered access to the deployment environment is not suitable for designing algorithms that can adapt to new situations over a long period. Instead, the authors propose a k-percent tuning approach, where only a portion of the experiment data can be used for hyperparameter tuning. The effectiveness of this approach is evaluated through an empirical study of DQN and SAC across various continuing and non-stationary domains. Surprisingly, agents that maintain network plasticity perform well even with restricted access to the environment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers are trying to make robots or computers learn and adapt over a long time. They want these machines to keep getting better even when they encounter new situations. Normally, we assume these machines have all the information they need to learn, but that’s not realistic. In real life, these machines might only get some information at first and then have to figure things out on their own. The authors are proposing a new way to test how well these machines do in this situation. They’re also testing different algorithms to see which ones work best. |
Keywords
* Artificial intelligence * Hyperparameter * Reinforcement learning
