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README.md

Experiment Notebooks

Neuro-nav includes a number of interactive jupyter notebooks. These demonstrate features of the library, as well as serve to reproduce various experiments in the literature.

General Notebooks

Notebook	Description	Referenced Work	Animal Model	Colab Link
Usage Tutorial	This notebook provides a basic usage tutorial of both environment types. This is the best place to start for those seeking to understand the features of neuro-nav.	N/A	N/A	Link
CCN 2022 Tutorial	This notebook was made to accompany the CCN 2022 Tutorial "Varieties of Human-like AI." It provides an overview of a number of the algorithms included in Neuro-Nav, and their properties.	N/A	N/A	Link
Deep RL Tutorial	This notebook provides a basic tutorial of deep reinforcement learning algorithms. It includes implementations of both PPO and SAC.	N/A	N/A	Link

Cognitive Neuroscience Notebooks

Notebook	Description	Referenced Work	Animal Model	Colab Link
Successor Representation Experiments	Demonstrates how to generate visualizations of the learned representations from agents utilizing a successor representation. These include value maps, successor "place" cells, and successor "grid" cells.	Stachenfeld et al., 2017	Rodent	Link
Grid Transfer Experiments	Evaluates various algorithms ability to adapt to changes in reward location or environment structure in goal-directed navigation tasks.	Russek et al., 2017	Rodent	Link
Graph Transfer Experiments (1) (2)	Evaluates various algorithms ability to adapt to changes in reward contingencies or transition dynamics in decision making tasks.	Momennejad et al., 2017	Human	Link (1), Link (2)
Temporal Community Experiments	Compares methods for learning representations which display temporal community structure. Utilizes a graph environment with local neighborhood structure.	Schapiro et al., 2013, Stachenfeld et al., 2017	Human	Link
Distributional Value Experiments	Compares a distributional and classical TD algorithm on a variable reward magnitude task. The distributional TD algorithm better captures behavior of dopamine neurons.	Dabney et al., 2020	Rodent	Link

Computational Psychiatry Notebooks

Notebook	Description	Referenced Work	Animal Model	Colab Link
Optimism & Pessimism Experiments	Demonstrates maladaptive learning when pessimistic value bootstrapping is used for distal state updates.	Zorowitz et al., 2020	Human	Link
Mood Experiments	Demonstrates learning mood as the temporal integral of advantages, both in its causes and effects.	Eldar et al., 2016, Bennett et al., 2022	Human	Link