Attention-Based Reward Shaping for Sparse and Delayed Rewards

2025-05-16

AI Summary

Sparse and delayed rewards severely hinder the practical deployment of reinforcement learning (RL). To address this, we propose ARES—the first fully offline reward shaping method that requires neither online interaction nor task-specific prior knowledge. Leveraging Transformer-based attention mechanisms, ARES automatically learns dense, shaped rewards from low-quality trajectory data (e.g., generated by random policies). It is algorithm-agnostic, seamlessly integrating with any RL framework, and exhibits strong robustness to extreme reward delay and data noise—without restricting applicability to goal-oriented tasks. Empirical evaluations across diverse environments and RL algorithms demonstrate that ARES significantly improves sample efficiency and convergence speed, enabling efficient solutions to tasks previously intractable or requiring prohibitively large datasets.

Technology Category

• Machine Learning: Reinforcement Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

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.

Problem

• Addresses sparse and delayed rewards in RL
• Proposes offline-trained attention-based reward shaping
• Enables learning in extreme reward delay scenarios

Innovation

• Uses transformer attention for reward shaping
• Works fully offline with minimal data
• Compatible with any RL algorithm