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README.md
SRDYNA_contour_episodes.pdf76.pdf
SRDyna_NatHumBeh_Exp1_learning.ipynb
SR_Dyna_no_action.py
SR_no_action.py
SRclass_nathum_exp1.py
SRdyna_nathum_exp1.py
SRonly_learner_NatHumBeh_Exp1.ipynb
contour_episodes129.pdf
exp1.gv.pdf
ida_envs.py

README.md

Predictive Representations

predictive_representations is a repository for successor representation codes and classes used in published papers and preprints.

The main notebook to look out for is: SRDyna_NatHumBeh_Exp1_learning

This notebook calls SRDyna_nathum_exp1.m, which itself uses the RL agent class in SR_dyna_no_action.py.

References

Momennejad I, Howard M (2018) Predicting the future with multi-scale successor representations.

Momennejad I, Otto RA, Daw N, Norman KA (2018) Offline replay supports planning in human reinforcement learning. eLife 2018;7:e32548.

Russek E*, Momennejad I*, Botvinick MM, Gershman SJ, Daw N (2017) Predictive representations can link model-based reinforcement learning to model-free mechanisms. Plos Comp Biol.

Momennejad I*, Russek E*, Cheong JH, Botvinick MM, Daw N, Gershman SJ (2017) The successor representation in human reinforcement learning: evidence from retrospective revaluation. Nature Human Behaviour, 1.

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