Summary of Comprehensive Overview Of Reward Engineering and Shaping in Advancing Reinforcement Learning Applications, by Sinan Ibrahim et al.
Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applications
by Sinan Ibrahim, Mostafa Mostafa, Ali Jnadi, Hadi Salloum, Pavel Osinenko
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 paper emphasizes the crucial role of reward engineering and reward shaping in enhancing the efficiency and effectiveness of reinforcement learning (RL) algorithms for real-world applications. By designing accurate reward functions and providing additional feedback, RL systems can accelerate convergence to optimal policies. However, limitations persist, including sparse and delayed rewards, complex environment modeling, and computational demands. Despite these challenges, recent advancements in deep learning have improved RL’s capability to handle high-dimensional state and action spaces, enabling applications in robotics, autonomous driving, and game playing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is a way for computers to make decisions by trying things and seeing what works best. This paper talks about how we can make these computer systems better at making decisions by designing special “rewards” that tell them when they’re doing something good or bad. It’s like giving a dog treats when it does something right! The problem is that in the real world, rewards are often hard to find and come late, which slows down learning. This paper reviews what we currently know about reinforcement learning, talks about the challenges, and suggests new ways to make it better. |
Keywords
» Artificial intelligence » Deep learning » Reinforcement learning
