Summary of Combining Automated Optimisation Of Hyperparameters and Reward Shape, by Julian Dierkes et al.
Combining Automated Optimisation of Hyperparameters and Reward Shape
by Julian Dierkes, Emma Cramer, Holger H. Hoos, Sebastian Trimpe
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper investigates the challenges of designing deep reinforcement learning (RL) models for novel practical applications where no prior knowledge about good hyperparameter configurations or reward functions exists. Current RL approaches heavily rely on expert-designed choices, which can be time-consuming and may not generalize well to new environments. The authors propose a methodology for simultaneously optimizing both hyperparameters and reward functions, demonstrating that the two are often mutually dependent. This approach is shown to significantly improve performance in half of the tested environments with only a minor increase in computational costs. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper explores how to make artificial intelligence (AI) learn from rewards better. Right now, making AI work for new tasks can be tricky because we need to choose the right settings and reward system. The researchers found that these two parts are connected and developed a way to improve both at the same time. This leads to better AI performance in some cases with only a little extra effort. |
Keywords
» Artificial intelligence » Hyperparameter » Reinforcement learning