aind-dynamic-foraging-models

aind-dynamic-foraging-models

AIND library for generative (RL) and descriptive (logistic regression) models of dynamic foraging tasks.

User documentation available on readthedocs.

Reinforcement Learning (RL) models with Maximum Likelihood Estimation (MLE) fitting

Overview

RL agents that can perform any dynamic foraging task in aind-behavior-gym and can fit behavior using MLE.

Code structure

• To add more generative models, please subclass DynamicForagingAgentMLEBase.

Implemented foragers

• ForagerQLearning: Simple Q-learning agents that incrementally update Q-values.
  • Available agent_kwargs:

      number_of_learning_rate: Literal[1, 2] = 2,
      number_of_forget_rate: Literal[0, 1] = 1,
      choice_kernel: Literal["none", "one_step", "full"] = "none",
      action_selection: Literal["softmax", "epsilon-greedy"] = "softmax",
    

• ForagerLossCounting: Loss counting agents with probabilistic loss_count_threshold.
  • Available agent_kwargs:

      win_stay_lose_switch: Literal[False, True] = False,
      choice_kernel: Literal["none", "one_step", "full"] = "none",
    

Here is the full list of available foragers:

Usage

• Jupyter notebook
• See also these unittest functions.

RL model playground

Play with the generative models here.

Logistic regression

See this demo notebook.

Choosing logistic regression models

Su 2022

$$ logit(p(c_r)) \sim RewardedChoice+UnrewardedChoice $$

Bari 2019

$$ logit(p(c_r)) \sim RewardedChoice+Choice $$

Hattori 2019

$$ logit(p(c_r)) \sim RewardedChoice+UnrewardedChoice+Choice $$

Miller 2021

$$ logit(p(c_r)) \sim Choice + Reward+ Choice*Reward $$

Encodings

• Ignored trials are removed

choice	reward	Choice	Reward	RewardedChoice	UnrewardedChoice	Choice * Reward
L	yes	-1	1	-1	0	-1
L	no	-1	-1	0	-1	1
R	yes	1	1	1	0	1
L	yes	-1	1	-1	0	-1
R	no	1	-1	0	1	-1
R	yes	1	1	1	0	1
L	no	-1	-1	0	-1	1

Some observations:

1. $RewardedChoice$ and $UnrewardedChoice$ are orthogonal
2. $Choice = RewardedChoice + UnrewardedChoice$
3. $Choice * Reward = RewardedChoice - UnrewardedChoice$

Comparison

	Su 2022	Bari 2019	Hattori 2019	Miller 2021
Equivalent to	RewC + UnrC	RewC + (RewC + UnrC)	RewC + UnrC + (RewC + UnrC)	(RewC + UnrC) + (RewC - UnrC) + Rew
Severity of multicollinearity	Not at all	Medium	Severe	Slight
Interpretation	Like a RL model with different learning rates on reward and unrewarded trials.	Like a RL model that only updates on rewarded trials, plus a choice kernel (tendency to repeat previous choices).	Like a RL model that has different learning rates on reward and unrewarded trials, plus a choice kernel (the full RL model from the same paper).	Like a RL model that has symmetric learning rates for rewarded and unrewarded trials, plus a choice kernel. However, the $Reward $ term seems to be a strawman assumption, as it means “if I get reward on any side, I’ll choose the right side more”, which doesn’t make much sense.
Conclusion	Probably the best	Okay	Not good due to the severe multicollinearity	Good

Regularization and optimization

The choice of optimizer depends on the penality term, as listed here.

• lbfgs - [l2, None]
• liblinear - [l1, l2]
• newton-cg - [l2, None]
• newton-cholesky - [l2, None]
• sag - [l2, None]
• saga - [elasticnet, l1, l2, None]

See also

• Foraging model simulation, model recovery, etc.: https://github.com/hanhou/Dynamic-Foraging

Installation

To install the software, run

pip install aind-dynamic-foraging-models

To develop the code, clone the repo to your local machine, and run

pip install -e .[dev]

Contributing

Linters and testing

There are several libraries used to run linters, check documentation, and run tests.

• Please test your changes using the coverage library, which will run the tests and log a coverage report:

coverage run -m unittest discover && coverage report

• Use interrogate to check that modules, methods, etc. have been documented thoroughly:

interrogate .

• Use flake8 to check that code is up to standards (no unused imports, etc.):

flake8 .

• Use black to automatically format the code into PEP standards:

black .

• Use isort to automatically sort import statements:

isort .

Pull requests

For internal members, please create a branch. For external members, please fork the repository and open a pull request from the fork. We'll primarily use Angular style for commit messages. Roughly, they should follow the pattern:

<type>(<scope>): <short summary>

where scope (optional) describes the packages affected by the code changes and type (mandatory) is one of:

• build: Changes that affect build tools or external dependencies (example scopes: pyproject.toml, setup.py)
• ci: Changes to our CI configuration files and scripts (examples: .github/workflows/ci.yml)
• docs: Documentation only changes
• feat: A new feature
• fix: A bugfix
• perf: A code change that improves performance
• refactor: A code change that neither fixes a bug nor adds a feature
• test: Adding missing tests or correcting existing tests

Semantic Release

The table below, from semantic release, shows which commit message gets you which release type when semantic-release runs (using the default configuration):

Commit message	Release type
fix(pencil): stop graphite breaking when too much pressure applied	Patch Fix Release, Default release
feat(pencil): add 'graphiteWidth' option	Minor Feature Release
perf(pencil): remove graphiteWidth option BREAKING CHANGE: The graphiteWidth option has been removed. The default graphite width of 10mm is always used for performance reasons.	Major Breaking Release (Note that the BREAKING CHANGE: token must be in the footer of the commit)

Documentation

To generate the rst files source files for documentation, run

sphinx-apidoc -o doc_template/source/ src 

Then to create the documentation HTML files, run

sphinx-build -b html doc_template/source/ doc_template/build/html

More info on sphinx installation can be found here.