mlsanityData to 7 Oct 2026

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

arXiv 2408.10215 ↗First posted 22 Jul 2024cs.LG cs.SY eess.SYcited by 34

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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.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Builds on 44 of its 103 references that are in the map. First posted on arXiv 22 Jul 2024.

Builds on

44 of 103 references.

References · 44

  1. 2017Deep reinforcement learning from human preferencescited by 3,846
  2. 2016Concrete Problems in AI Safetycited by 1,582
  3. 2017Minimax Regret Bounds for Reinforcement Learningcited by 500
  4. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 388
  5. 2016Benchmarking Deep Reinforcement Learning for Continuous Controlcited by 361
  6. 2018Reward learning from human preferences and demonstrations in Ataricited by 314
  7. 2016#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learningcited by 307
  8. 2017Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewardscited by 302
  9. 2021PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-trainingcited by 298
  10. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progresscited by 294
  11. 2017Inverse Reward Designcited by 246
  12. 2021A Survey of Human-in-the-loop for Machine Learningcited by 214
32 more
  1. 2015Maximum Entropy Deep Inverse Reinforcement Learningcited by 173
  2. 2023Text2Reward: Reward Shaping with Language Models for Reinforcement Learningcited by 153
  3. 2019End-to-End Robotic Reinforcement Learning without Reward Engineeringcited by 115
  4. 2019Using Natural Language for Reward Shaping in Reinforcement Learningcited by 114
  5. 2020Learning to Utilize Shaping Rewards: A New Approach of Reward Shapingcited by 112
  6. 2018On Learning Intrinsic Rewards for Policy Gradient Methodscited by 100
  7. 2022SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learningcited by 96
  8. 2018Reinforcement Learning with Perturbed Rewardscited by 76
  9. 2017Towards Generalization and Simplicity in Continuous Controlcited by 65
  10. 2022Reward Uncertainty for Exploration in Preference-based Reinforcement Learningcited by 60
  11. 2022Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexitycited by 59
  12. 2021Scalar reward is not enough: A response to Silver, Singh, Precup and Sutton (2021)cited by 55
  13. 2023Uncertainty-Penalized Reinforcement Learning from Human Feedback with Diverse Reward LoRA Ensemblescited by 55
  14. 2020Model-Based Inverse Reinforcement Learning from Visual Demonstrationscited by 52
  15. 2017Traffic flow optimization using a quantum annealercited by 29
  16. 2020Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Taskscited by 28
  17. 2023Reparameterized Policy Learning for Multimodal Trajectory Optimizationcited by 20
  18. 2023Reward Shaping for Happier Autonomous Cyber Security Agentscited by 19
  19. 2022Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Trafficcited by 15
  20. 2022Admissible Policy Teaching through Reward Designcited by 12
  21. 2022Reinforcement Learned Distributed Multi-Robot Navigation with Reciprocal Velocity Obstacle Shaped Rewardscited by 11
  22. 2020Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environmentcited by 10
  23. 2022Automated Reinforcement Learning: An Overviewcited by 9
  24. 2019A new Potential-Based Reward Shaping for Reinforcement Learning Agentcited by 8
  25. 2023LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Frameworkcited by 7
  26. 2024Barrier Functions Inspired Reward Shaping for Reinforcement Learningcited by 7
  27. 2023Guaranteeing Control Requirements via Reward Shaping in Reinforcement Learningcited by 5
  28. 2024Combining Automated Optimisation of Hyperparameters and Reward Shapecited by 5
  29. 2022Natural Policy Gradients In Reinforcement Learning Explainedcited by 3
  30. 2023Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approachcited by 2
  31. 2024Sim-to-Real Transfer of Deep Reinforcement Learning Agents for Online Coverage Path Planningcited by 2
  32. 2024Robust Reward Design for Markov Decision Processescited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 20; 2026: 13.

34 papers.

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  1. 2024Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedbackcited by 10
  2. 2026Save the Good Prefix: Precise Error Penalization via Process-Supervised RL to Enhance LLM Reasoningcited by 10
  3. 2025Enhancing Agentic RL with Progressive Reward Shaping and Value-based Sampling Policy Optimizationcited by 9
  4. 2025A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulatorcited by 7
  5. 2025The Crucial Role of Problem Formulation in Real-World Reinforcement Learningcited by 6
  6. 2025Automatic Reward Shaping from Confounded Offline Datacited by 6
  7. 2025Stabilizing Long-term Multi-turn Reinforcement Learning with Gated Rewardscited by 4
  8. 2025Ethical AI: Towards Defining a Collective Evaluation Frameworkcited by 3
  9. 2025Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG-RLcited by 3
  10. 2025Demystifying the Mechanisms Behind Emergent Exploration in Goal-conditioned RLcited by 3
  11. 2025From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learningcited by 2
  12. 2025Timing the Match: A Deep Reinforcement Learning Approach for Ride-Hailing and Ride-Pooling Servicescited by 2
22 more
  1. 2025Leveraging LLMs for reward function design in reinforcement learning control taskscited by 2
  2. 2026Confounding Robust Continuous Control via Automatic Reward Shapingcited by 2
  3. 2025An Automated Reinforcement Learning Reward Design Framework with Large Language Model for Cooperative Platoon Coordinationcited by 1
  4. 2025Exploration by Random Reward Perturbationcited by 1
  5. 2025Proactive Constrained Policy Optimization with Preemptive Penaltycited by 1
  6. 2025Closing the Intent-to-Behavior Gap via Fulfillment Priority Logiccited by 0
  7. 2025Time Reversal Symmetry for Efficient Robotic Manipulations in Deep Reinforcement Learningcited by 0
  8. 2025Balancing Specialization and Centralization: A Multi-Agent Reinforcement Learning Benchmark for Sequential Industrial Controlcited by 0
  9. 2025Reward Engineering for Spatial Epidemic Simulations: A Reinforcement Learning Platform for Individual Behavioral Learningcited by 0
  10. 2025Automating the Refinement of Reinforcement Learning Specificationscited by 0
  11. 2025SCOPE: Language Models as One-Time Teacher for Hierarchical Planning in Text Environmentscited by 0
  12. 2026CORE: Code-based Inverse Self-Training Framework with Graph Expansion for Virtual Agentscited by 0
  13. 2026SSL: Sweet Spot Learning for Differentiated Guidance in Agentic Optimizationcited by 0
  14. 2026Live LTL Progress Tracking: Towards Task-Based Explorationcited by 0
  15. 2026Zero Shot Coordination for Sparse Reward Tasks with Diverse Reward Shapingscited by 0
  16. 2026Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimizationcited by 0
  17. 2026Action-Conditioned Risk Gating for Safety-Critical Control under Partial Observabilitycited by 0
  18. 2026Physics-informed Goal-Conditioned Reinforcement Learning under Hybrid Contact Dynamicscited by 0
  19. 2026Generative Modeling of Discrete Latent Structures via Dynamic Policy Gradientscited by 0
  20. 2026Model-Free Reinforcement Learning Control for Resilient Cyber-Physical Systemscited by 0
  21. 2026Towards Socially Compliant Navigation in Deep Reinforcement Learning via Proxemics-Based Reward Modelingcited by 0
  22. 2026PHIRL: Aligning Learned Rewards with Task Progress for Inverse Reinforcement Learningcited by 0