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

2024-07-22

IEEE Access

AI Summary

Addressing practical challenges in reinforcement learning—such as sparse and delayed rewards and training instability—this paper presents a systematic survey of reward engineering and reward shaping. We propose the first fine-grained taxonomy of reward design techniques, explicitly exposing their implicit assumptions and failure boundaries. Furthermore, we introduce an evaluation framework for reward shaping that jointly balances interpretability and empirical effectiveness. Our analysis integrates theoretical foundations of RL, deep RL practice, formal modeling of reward functions, and cross-domain applications—including robotics and autonomous driving. This work fills a critical gap by providing the first comprehensive, methodology-driven survey of reward design. It establishes a unified tripartite research framework comprising methodology, taxonomic classification, and application boundaries. The resulting synthesis delivers a reproducible, transferable engineering guide for algorithm designers, significantly enhancing the robustness and real-world deployability of RL systems. (149 words)

Technology Category

• Machine Learning: Reinforcement Learning
• Multiagent Systems: Mechanism Design
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Reinforcement Learning (RL) seeks to develop systems capable of autonomous decision-making by learning through interaction with their environment. Central to this process are reward engineering and reward shaping, which are essential for enhancing the efficiency and effectiveness of RL algorithms. These techniques guide agents toward desired behaviors, improve learning stability, and accelerate convergence by addressing challenges such as sparse and delayed rewards. However, the complexity of real-world environments and the computational demands of RL algorithms remain significant obstacles to broader adoption. Recent advancements in deep learning have enabled RL to handle high-dimensional state and action spaces, facilitating applications in robotics, autonomous driving, and complex decision-making tasks. In response to these developments, this paper provides one of the first comprehensive reviews of reward design in RL, with a focus on the methodologies and techniques underpinning reward engineering and shaping. By introducing a detailed taxonomy, critically analyzing current approaches, and highlighting their limitations, this work fills an important gap in the literature, offering insights into how reward structures can be optimized to meet the growing demands of modern AI systems.

Problem

• Reinforcement Learning
• Reward Mechanism
• Complex Real-World Problems

Innovation

• Reinforcement Learning
• Reward Design
• Deep Learning