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.
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.