Reinforcement Learning and the Reward Engineering Principle

By Dan Dewey. 2014

151 citations recorded by the source corpus.

Subfields: Reinforcement Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Reinforcement Learning

1. R. West
2. João C. P. Reis
3. William Koch
4. Azer Bestavros
5. Leonor Santos
6. Gil Gonçalves
7. Renato Mancuso
8. Liliana Antão

Citations in the corpus

• Reinforcement Learning for UAV Attitude Control (2018, background): “For instance, what will be the necessary adaptation rate, and what sensor data can be collected from the real world to update the RL environment? C3: Reward Engineering: In RL, reward engineering is the process of designing a reward system to provide the agent a signal showing that they are doing th”
• Reward Shaping Study for Sub-Goal Based Robotic Tasks (2022, background): “Given this, some efforts have been made to study and to improve reward functions [4]–[6].”
• Adversarial Deep Reinforcement Learning in Portfolio Management (2018, background): “In fact, reward engineering is one of the core topics in designing reinforcement learning algorithms [22].”