The use of Potential Based Reward Shaping (PBRS) has shown great promise in the ongoing research effort to tackle sample inefficiency in Reinforcement Learning (RL). However, the choice of the potential function is critical for this technique to be effective. Additionally, RL techniques are usually constrained to use a finite horizon for computational limitations. This introduces a bias when using PBRS, thus adding an additional layer of complexity. In this paper, we leverage abstractions to automatically produce a "good" potential function. We analyse the bias induced by finite horizons in the context of PBRS producing novel insights. Finally, to asses sample efficiency and performance impact, we evaluate our approach on four environments including a goal-oriented navigation task and three Arcade Learning Environments (ALE) games demonstrating that we can reach the same level of performance as CNN-based solutions with a simple fully-connected network.

利用抽象技术在有限时间内生成“好”的潜力函数，分析在Potential Based Reward Shaping中有限时间引入的偏差，并在四个不同环境中进行评估，证明我们可以通过简单的全连接网络达到与基于CNN的解决方案相同的性能水平。

强化学习中抽象和基于潜力的奖励塑形的样本效率研究