# Attention-Based Reward Shaping for Sparse and Delayed Rewards

arXiv: https://arxiv.org/abs/2505.10802 · HTML: https://mlsanity.com/paper/2505.10802/

## 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.

## 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, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations](https://mlsanity.com/paper/1904.06387/) (2019)
- [Defining and Characterizing Reward Hacking](https://mlsanity.com/paper/2209.13085/) (2022)
- [Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards](https://mlsanity.com/paper/1911.01417/) (2019)
- [Dense Reward for Free in Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2402.00782/) (2024)
- [Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration](https://mlsanity.com/paper/2202.04628/) (2022)
- [DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning](https://mlsanity.com/paper/2203.06662/) (2022)
- [Self-Supervised Online Reward Shaping in Sparse-Reward Environments](https://mlsanity.com/paper/2103.04529/) (2021)
- [Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution](https://mlsanity.com/paper/2009.14108/) (2020)
- [Learning Long-Term Reward Redistribution via Randomized Return Decomposition](https://mlsanity.com/paper/2111.13485/) (2021)
- [Learning Guidance Rewards with Trajectory-space Smoothing](https://mlsanity.com/paper/2010.12718/) (2020)
- [Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learning](https://mlsanity.com/paper/1905.13420/) (2019)
- [Synthetic Returns for Long-Term Credit Assignment](https://mlsanity.com/paper/2102.12425/) (2021)
- [DrS: Learning Reusable Dense Rewards for Multi-Stage Tasks](https://mlsanity.com/paper/2404.16779/) (2024)
- [Learning to Shape Rewards using a Game of Two Partners](https://mlsanity.com/paper/2103.09159/) (2021)
- [Predictive Coding for Boosting Deep Reinforcement Learning with Sparse Rewards](https://mlsanity.com/paper/1912.13414/) (2019)
- [Self-Imitation Learning for Robot Tasks with Sparse and Delayed Rewards](https://mlsanity.com/paper/2010.06962/) (2020)

## Built on by (most cited first, 5 of 5)

- [Self-Rewarding Vision-Language Model via Reasoning Decomposition](https://mlsanity.com/paper/2508.19652/) (2025)
- [PRISM: Parallel Reward Integration with Symmetry for MORL](https://mlsanity.com/paper/2602.18277/) (2026)
- [Who Gets the Reward & Who Gets the Blame? Evaluation-Aligned Training Signals for Multi-LLM Agents](https://mlsanity.com/paper/2511.10687/) (2025)
- [STO-RL: Offline RL under Sparse Rewards via LLM-Guided Subgoal Temporal Order](https://mlsanity.com/paper/2601.08107/) (2026)
- [Token-weighted Direct Preference Optimization with Attention](https://mlsanity.com/paper/2605.21883/) (2026)

## 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.