  Based on the predictive map theory of spatial learning in animals, this study
delves into the dynamics of Successor Feature (SF) and Predecessor Feature (PF)
algorithms within noisy environments. Utilizing Q-learning and Q($\lambda$)
learning as benchmarks for comparative analysis, our investigation yielded
unexpected outcomes. Contrary to prevailing expectations and previous
literature where PF demonstrated superior performance, our findings reveal that
in noisy environments, PF did not surpass SF. In a one-dimensional grid world,
SF exhibited superior adaptability, maintaining robust performance across
varying noise levels. This trend of diminishing performance with increasing
noise was consistent across all examined algorithms, indicating a linear
degradation pattern. The scenario shifted in a two-dimensional grid world,
where the impact of noise on algorithm performance demonstrated a non-linear
relationship, influenced by the $\lambda$ parameter of the eligibility trace.
This complexity suggests that the interaction between noise and algorithm
efficacy is tied to the environmental dimensionality and specific algorithmic
parameters. Furthermore, this research contributes to the bridging discourse
between computational neuroscience and reinforcement learning (RL), exploring
the neurobiological parallels of SF and PF learning in spatial navigation.
Despite the unforeseen performance trends, the findings enrich our
comprehension of the strengths and weaknesses inherent in RL algorithms. This
knowledge is pivotal for advancing applications in robotics, gaming AI, and
autonomous vehicle navigation, underscoring the imperative for continued
exploration into how RL algorithms process and learn from noisy inputs.
