We’re excited to share that our study, “Investigating Transfer Learning in Noisy Environments: A Study of Predecessor and Successor Features in Spatial Learning Using a T-Maze,” has been published in Sensors!
In this study, we delve into the critical challenge of noise in reinforcement learning environments. Specifically, we focus on how transfer learning algorithms—Predecessor and Successor Features (PFs and SFs)—perform in spatial learning tasks under noisy conditions.
Why Is This Study Important?
Noise is a significant factor in real-world applications, especially for systems that rely on accurate sensor data to make decisions. Our study provides valuable insights into how learning models can be optimized in such conditions by fine-tuning hyperparameters like the reward learning rate and eligibility trace decay. We discovered that these parameters significantly influence the agent’s adaptability and learning efficiency, providing practical guidelines for improving the performance of sensor-driven systems in noisy, dynamic environments.
Key Takeaways from Our Study:
- We evaluated the impact of various hyperparameters on adaptive behavior in noisy spatial learning environments using a T-maze, revealing their critical role in learning efficiency.
- We introduced a framework based on PF and SF to compare and quantify sensitivity to noise and adaptation in reinforcement learning models.
- We identified the most robust hyperparameter configurations that optimize performance in noisy and variable conditions, providing practical insights for improving system adaptability.
This research offers valuable contributions to the fields of sensor systems, robotics, and autonomous navigation by enhancing learning models’ resilience to noise.
Read the full paper here to learn more! Additionally, the code used in the study can be accessed here.