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

arXiv: https://arxiv.org/abs/2408.10215 · HTML: https://mlsanity.com/paper/2408.10215/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 44 of its 103 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 30 of 44)

- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Minimax Regret Bounds for Reinforcement Learning](https://mlsanity.com/paper/1703.05449/) (2017)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Benchmarking Deep Reinforcement Learning for Continuous Control](https://mlsanity.com/paper/1604.06778/) (2016)
- [Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards](https://mlsanity.com/paper/1707.08817/) (2017)
- [Reward learning from human preferences and demonstrations in Atari](https://mlsanity.com/paper/1811.06521/) (2018)
- [#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning](https://mlsanity.com/paper/1611.04717/) (2016)
- [PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training](https://mlsanity.com/paper/2106.05091/) (2021)
- [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018)
- [Inverse Reward Design](https://mlsanity.com/paper/1711.02827/) (2017)
- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [Maximum Entropy Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/1507.04888/) (2015)
- [Text2Reward: Reward Shaping with Language Models for Reinforcement Learning](https://mlsanity.com/paper/2309.11489/) (2023)
- [End-to-End Robotic Reinforcement Learning without Reward Engineering](https://mlsanity.com/paper/1904.07854/) (2019)
- [Using Natural Language for Reward Shaping in Reinforcement Learning](https://mlsanity.com/paper/1903.02020/) (2019)
- [Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping](https://mlsanity.com/paper/2011.02669/) (2020)
- [On Learning Intrinsic Rewards for Policy Gradient Methods](https://mlsanity.com/paper/1804.06459/) (2018)
- [SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning](https://mlsanity.com/paper/2203.10050/) (2022)
- [Reinforcement Learning with Perturbed Rewards](https://mlsanity.com/paper/1810.01032/) (2018)
- [Towards Generalization and Simplicity in Continuous Control](https://mlsanity.com/paper/1703.02660/) (2017)
- [Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity](https://mlsanity.com/paper/2210.09579/) (2022)
- [Reward Uncertainty for Exploration in Preference-based Reinforcement Learning](https://mlsanity.com/paper/2205.12401/) (2022)
- [Uncertainty-Penalized Reinforcement Learning from Human Feedback with Diverse Reward LoRA Ensembles](https://mlsanity.com/paper/2401.00243/) (2023)
- [Scalar reward is not enough: A response to Silver, Singh, Precup and Sutton (2021)](https://mlsanity.com/paper/2112.15422/) (2021)
- [Model-Based Inverse Reinforcement Learning from Visual Demonstrations](https://mlsanity.com/paper/2010.09034/) (2020)
- [Traffic flow optimization using a quantum annealer](https://mlsanity.com/paper/1708.01625/) (2017)
- [Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks](https://mlsanity.com/paper/2007.01498/) (2020)
- [Reparameterized Policy Learning for Multimodal Trajectory Optimization](https://mlsanity.com/paper/2307.10710/) (2023)
- [Reward Shaping for Happier Autonomous Cyber Security Agents](https://mlsanity.com/paper/2310.13565/) (2023)

## Built on by (most cited first, 30 of 38)

- [Save the Good Prefix: Precise Error Penalization via Process-Supervised RL to Enhance LLM Reasoning](https://mlsanity.com/paper/2601.18984/) (2026)
- [Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback](https://mlsanity.com/paper/2410.23022/) (2024)
- [Enhancing Agentic RL with Progressive Reward Shaping and Value-based Sampling Policy Optimization](https://mlsanity.com/paper/2512.07478/) (2025)
- [A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator](https://mlsanity.com/paper/2509.08221/) (2025)
- [The Crucial Role of Problem Formulation in Real-World Reinforcement Learning](https://mlsanity.com/paper/2503.20442/) (2025)
- [Automatic Reward Shaping from Confounded Offline Data](https://mlsanity.com/paper/2505.11478/) (2025)
- [Stabilizing Long-term Multi-turn Reinforcement Learning with Gated Rewards](https://mlsanity.com/paper/2508.10548/) (2025)
- [Demystifying the Mechanisms Behind Emergent Exploration in Goal-conditioned RL](https://mlsanity.com/paper/2510.14129/) (2025)
- [From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learning](https://mlsanity.com/paper/2501.17842/) (2025)
- [Ethical AI: Towards Defining a Collective Evaluation Framework](https://mlsanity.com/paper/2506.00233/) (2025)
- [Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG-RL](https://mlsanity.com/paper/2509.01058/) (2025)
- [Timing the Match: A Deep Reinforcement Learning Approach for Ride-Hailing and Ride-Pooling Services](https://mlsanity.com/paper/2503.13200/) (2025)
- [Proactive Constrained Policy Optimization with Preemptive Penalty](https://mlsanity.com/paper/2508.01883/) (2025)
- [Leveraging LLMs for reward function design in reinforcement learning control tasks](https://mlsanity.com/paper/2511.19355/) (2025)
- [Confounding Robust Continuous Control via Automatic Reward Shaping](https://mlsanity.com/paper/2602.10305/) (2026)
- [An Automated Reinforcement Learning Reward Design Framework with Large Language Model for Cooperative Platoon Coordination](https://mlsanity.com/paper/2504.19480/) (2025)
- [Exploration by Random Reward Perturbation](https://mlsanity.com/paper/2506.08737/) (2025)
- [Reward-Augmented Reinforcement Learning for Continuous Control in Precision Autonomous Parking via Policy Optimization Methods](https://mlsanity.com/paper/2507.19642/) (2025)
- [Keyframe-Guided Structured Rewards for Reinforcement Learning in Long-Horizon Laboratory Robotics](https://mlsanity.com/paper/2603.00719/) (2026)
- [Closing the Intent-to-Behavior Gap via Fulfillment Priority Logic](https://mlsanity.com/paper/2503.05818/) (2025)
- [Time Reversal Symmetry for Efficient Robotic Manipulations in Deep Reinforcement Learning](https://mlsanity.com/paper/2505.13925/) (2025)
- [Balancing Specialization and Centralization: A Multi-Agent Reinforcement Learning Benchmark for Sequential Industrial Control](https://mlsanity.com/paper/2510.20408/) (2025)
- [Reward Engineering for Spatial Epidemic Simulations: A Reinforcement Learning Platform for Individual Behavioral Learning](https://mlsanity.com/paper/2511.18000/) (2025)
- [Automating the Refinement of Reinforcement Learning Specifications](https://mlsanity.com/paper/2512.01047/) (2025)
- [SCOPE: Language Models as One-Time Teacher for Hierarchical Planning in Text Environments](https://mlsanity.com/paper/2512.09897/) (2025)
- [CORE: Code-based Inverse Self-Training Framework with Graph Expansion for Virtual Agents](https://mlsanity.com/paper/2601.02201/) (2026)
- [SSL: Sweet Spot Learning for Differentiated Guidance in Agentic Optimization](https://mlsanity.com/paper/2601.22491/) (2026)
- [Live LTL Progress Tracking: Towards Task-Based Exploration](https://mlsanity.com/paper/2604.17106/) (2026)
- [Zero Shot Coordination for Sparse Reward Tasks with Diverse Reward Shapings](https://mlsanity.com/paper/2604.25076/) (2026)
- [Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization](https://mlsanity.com/paper/2605.12771/) (2026)

## Abstract (arXiv)

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.