Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applications

Understand

The aim of Reinforcement Learning (RL) in real-world applications is to create systems capable of making autonomous decisions by learning from their environment through trial and error.

• This paper emphasizes the importance of reward engineering and reward shaping in enhancing the efficiency and effectiveness of reinforcement learning algorithms.
• Reward engineering involves designing reward functions that accurately reflect the desired outcomes, while reward shaping provides additional feedback to guide the learning process, accelerating convergence to optimal policies.
• Despite significant advancements in reinforcement learning, several limitations persist.