Attention-Based Reward Shaping for Sparse and Delayed Rewards

• URL: http://arxiv.org/abs/2505.10802v1
• Date: Fri, 16 May 2025 02:43:05 GMT
• Title: Attention-Based Reward Shaping for Sparse and Delayed Rewards
• Authors: Ian Holmes, Min Chi,
• Abstract summary: We propose Attention-based REward Shaping (ARES) to generate shaped rewards for any environment.<n>ARES 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.<n>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.
• Score: 7.811459544911894
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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