Predictive map theory, one of the theories explaining spatial learning in animals, is based on successor representation (SR) learning algorithms. In the real world, agents such as animals and robots are subjected to noisy observations, which can lead to suboptimal actions or even failure during learning. In this study, we compared the performance of Successor Features (SFs) and Predecessor Features (PFs) algorithms in a noisy one-dimensional maze environment. Our results demonstrated that PFs consistently outperformed SFs in terms of cumulative reward and average step length, with higher resilience to noise. This superiority could be due to PFs' ability to transmit temporal difference errors to more preceding states. We also discuss the biological mechanisms involved in PFs learning for spatial navigation. This study contributes to the theoretical research on computational neuroscience using reinforcement learning algorithms, and highlights the practical potential of PFs in robotics, game AI, and autonomous vehicle navigation.

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