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

TL;DR

This paper reviews how reward engineering and shaping techniques improve reinforcement learning efficiency, discusses current challenges like sparse rewards, and highlights recent advancements enabling complex real-world applications.

Contribution

It provides a comprehensive overview of reward engineering and shaping methods, analyzing limitations and recent progress in reinforcement learning for practical applications.

Findings

Reward shaping accelerates convergence in RL algorithms.

Deep learning advancements enable RL in complex environments.

Challenges include sparse rewards and high computational demands.

Abstract

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. One key challenge is the sparse and delayed nature of rewards in many real-world scenarios, which can hinder learning progress. Additionally, the complexity of accurately modeling real-world environments and the computational demands of reinforcement learning algorithms remain substantial obstacles. On the other hand, recent advancements in deep learning and neural networks have significantly improved the capability of reinforcement learning systems to handle high-dimensional state and action spaces, enabling their application to complex tasks such as robotics, autonomous driving, and game playing. This paper provides a comprehensive review of the current state of reinforcement learning, focusing on the methodologies and techniques used in reward engineering and reward shaping. It critically analyzes the limitations and recent advancements in the field, offering insights into future research directions and potential applications in various domains.