mlsanityData to 14 Oct 2026

Attention-Based Reward Shaping for Sparse and Delayed Rewards

arXiv 2505.10802 ↗First posted 16 May 2025cs.LG cs.AIcited by 5

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

Sparse and delayed reward functions pose a significant obstacle for real-world Reinforcement Learning (RL) applications. In this work, we propose Attention-based REward Shaping (ARES), a general and robust algorithm which uses a transformer's attention mechanism to generate shaped rewards and create a dense reward function for any environment. ARES requires a set of episodes and their final returns as input. It can be trained entirely offline and is able to generate meaningful shaped rewards even when using small datasets or episodes produced by agents taking random actions. ARES is compatible with any RL algorithm and can handle any level of reward sparsity. In our experiments, we focus on the most challenging case where rewards are fully delayed until the end of each episode. We evaluate ARES across a diverse range of environments, widely used RL algorithms, and baseline methods to assess the effectiveness of the shaped rewards it produces. Our results show that ARES can significantly improve learning in delayed reward settings, enabling RL agents to train in scenarios that would otherwise require impractical amounts of data or even be unlearnable. To our knowledge, ARES is the first approach that works fully offline, remains robust to extreme reward delays and low-quality data, and is not limited to goal-based tasks.

Key facts. Cited by 5 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 21 of its 28 references that are in the map. First posted on arXiv 16 May 2025.

Builds on

21 of 28 references.

References · 21

  1. 2017Attention Is All You Needcited by 60,361
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2017Hindsight Experience Replaycited by 1,136
  4. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,103
  5. 2018Learning Dexterous In-Hand Manipulationcited by 687
  6. 2019Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observationscited by 260
  7. 2022Defining and Characterizing Reward Hackingcited by 203
  8. 2019Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewardscited by 78
  9. 2024Dense Reward for Free in Reinforcement Learning from Human Feedbackcited by 78
  10. 2022Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstrationcited by 54
  11. 2022DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learningcited by 41
  12. 2021Self-Supervised Online Reward Shaping in Sparse-Reward Environmentscited by 37
9 more
  1. 2020Align-RUDDER: Learning From Few Demonstrations by Reward Redistributioncited by 36
  2. 2021Learning Long-Term Reward Redistribution via Randomized Return Decompositioncited by 33
  3. 2020Learning Guidance Rewards with Trajectory-space Smoothingcited by 30
  4. 2019Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learningcited by 27
  5. 2021Synthetic Returns for Long-Term Credit Assignmentcited by 27
  6. 2024DrS: Learning Reusable Dense Rewards for Multi-Stage Taskscited by 10
  7. 2021Learning to Shape Rewards using a Game of Two Partnerscited by 9
  8. 2019Predictive Coding for Boosting Deep Reinforcement Learning with Sparse Rewardscited by 4
  9. 2020Self-Imitation Learning for Robot Tasks with Sparse and Delayed Rewardscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025Self-Rewarding Vision-Language Model via Reasoning Decompositioncited by 97
  2. 2026PRISM: Parallel Reward Integration with Symmetry for MORLcited by 1
  3. 2025Who Gets the Reward & Who Gets the Blame? Evaluation-Aligned Training Signals for Multi-LLM Agentscited by 0
  4. 2026STO-RL: Offline RL under Sparse Rewards via LLM-Guided Subgoal Temporal Ordercited by 0
  5. 2026Token-weighted Direct Preference Optimization with Attentioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Dense Dynamics-Aware Reward Synthesis: Integrating Prior Experience with Demonstrations
  2. 2025Shaping Sparse Rewards in Reinforcement Learning: A Semi-supervised Approach
  3. 2024Highly Efficient Self-Adaptive Reward Shaping for Reinforcement Learning
  4. 2024Adaptive Reward Design for Reinforcement Learning
  5. 2024Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applications
  6. 2024Beyond Simple Sum of Delayed Rewards: Non-Markovian Reward Modeling for Reinforcement Learning
  7. 2026A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning
  8. 2025TimeRewarder: Learning Dense Reward from Passive Videos via Frame-wise Temporal Distance