Summary of Effective Reward Specification in Deep Reinforcement Learning, by Julien Roy
Effective Reward Specification in Deep Reinforcement Learning
by Julien Roy
First submitted to arxiv on: 10 Dec 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 The paper explores the challenges and limitations of Deep Reinforcement Learning (DRL) in complex sequential decision-making problems. DRL combines deep learning’s ability to process rich input signals with reinforcement learning’s adaptability across diverse control tasks. The goal is to maximize cumulative reward, but improper reward specification can lead to misaligned agent behavior and inefficient learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how AI algorithms called Reinforcement Learning agents try to find the best solution for a problem by maximizing rewards. But this can be tricky because if the rewards are not set up correctly, the agent might do something unexpected or waste time trying to learn. The problem gets even harder when dealing with complex tasks that involve many different parts. |
Keywords
» Artificial intelligence » Deep learning » Reinforcement learning
