Summary of Informativeness Of Reward Functions in Reinforcement Learning, by Rati Devidze et al.
Informativeness of Reward Functions in Reinforcement Learning
by Rati Devidze, Parameswaran Kamalaruban, Adish Singla
First submitted to arxiv on: 10 Feb 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 In this paper, researchers tackle the challenge of designing informative and interpretable reward functions for reinforcement learning agents. The authors propose a novel approach to quantify the informativeness of a reward function based on an agent’s current policy, allowing for adaptive design of rewards that speed up convergence. This innovation can be applied in expert-driven settings where an expert or teacher provides guidance to the agent. By leveraging this new criterion, the researchers demonstrate improved performance and interpretability on two navigation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists are trying to help machines learn better by creating special rewards that tell them what behavior is good or bad. They want these rewards to be helpful and easy to understand. To do this, they came up with a new way to measure how well the reward will work based on what the machine has learned so far. This helps experts who teach machines to create better rewards that make the machines learn faster. |
Keywords
* Artificial intelligence * Reinforcement learning
