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Python implementation of SR-Dyna from Russek, Momennejad et al 2017

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SR-Dyna Python Implementation

A Python/numpy implementation of SR-Dyna from [1] by Russek, Momennejad, Botvinick, Gershman and Daw, 2017.

Russek's Matlab code and Momennejad's Python tutorials were used for reference to fill in gaps when not fully specified.

Anim from Revaluation Task

Usage

Install requirements pip install -r requirements.txt, then run one of the task jupyter notebooks jupyter notebook ./sr-dyna-latent-learning.ipynb. Output animations are saved to ./out.

Main gridworld and algorithm code is in srdyna.py. Each notebook runs simulations attempted to match experiments presented in [1].

References

  1. Russek, E. M., Momennejad, I., Botvinick, M. M., Gershman, S. J., & Daw, N. D. (2017). Predictive representations can link model-based reinforcement learning to model-free mechanisms. PLoS computational biology, 13(9), e1005768.

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