digantamisra98 / Sequoia

The Research Tree - A playground for research at the intersection of Continual, Reinforcement, and Self-Supervised Learning.

Sequoia - The Research Tree

A Playground for research at the intersection of Continual, Reinforcement, and Self-Supervised Learning.

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Please note: This is still very much a Work-In-Progress!

Motivation:

Most applied ML research generally either proposes new Settings (research problems), new Methods (solutions to such problems), or both.

• When proposing new Settings, researchers almost always have to reimplement or heavily modify existing solutions before they can be applied onto their new problem.

• Likewise, when creating new Methods, it's often necessary to first re-create the experimental setting of other baseline papers, or even the baseline methods themselves, as experimental conditions may be slightly different between papers!

The goal of this repo is to:

• Organize various research Settings into an inheritance hierarchy (a tree!), with more general, challenging settings with few assumptions at the top, and more constrained problems at the bottom.

• Provide a mechanism for easily reusing existing solutions (Methods) onto new Settings through Polymorphism!

• Allow researchers to easily create new, general Methods and quickly gather results on a multitude of Settings, ranging from Supervised to Reinforcement Learning!

Installation

Requires python >= 3.7

1. Clone the repo:

   $ git clone https://www.github.com/lebrice/Sequoia.git
   $ cd Sequoia

2. Optional: Create the conda environment (only once):

   $ conda env create -f environment.yaml
   $ conda activate sequoia

3. Install the dependencies:

   $ pip install -e .

Additional Installation Steps for Mac

Install the latest XQuartz app from here: https://www.xquartz.org/releases/index.html

Then run the following commands on the terminal:

mkdir /tmp/.X11-unix 
sudo chmod 1777 /tmp/.X11-unix 
sudo chown root /tmp/.X11-unix/

Documentation overview:

• Getting Started / Examples (read this first!)

• Runing Experiments (below)

• Settings overview

• Methods overview

Current Settings & Assumptions:

Setting	RL vs SL	clear task boundaries?	Task boundaries given?	Task labels at training time?	task labels at test time	Stationary context?	Fixed action space
Continual RL	RL	no	no	no	no	no	no(?)
Discrete Task-Agnostic RL	RL	yes	yes	no	no	no	no(?)
Incremental RL	RL	yes	yes	yes	no	no	no(?)
Task-Incremental RL	RL	yes	yes	yes	yes	no	no(?)
Traditional RL	RL	yes	yes	yes	no	yes	no(?)
Multi-Task RL	RL	yes	yes	yes	yes	yes	no(?)
Continual SL	SL	no	no	no	no	no	no
Discrete Task-Agnostic SL	SL	yes	no	no	no	no	no
(Class) Incremental SL	SL	yes	yes	no	no	no	no
Domain-Incremental SL	SL	yes	yes	yes	no	no	yes
Task-Incremental SL	SL	yes	yes	yes	yes	no	no
Traditional SL	SL	yes	yes	yes	no	yes	no
Multi-Task SL	SL	yes	yes	yes	yes	yes	no

Notes

• Active / Passive: Active settings are Settings where the next observation depends on the current action, i.e. where actions influence future observations, e.g. Reinforcement Learning. Passive settings are Settings where the current actions don't influence the next observations (e.g. Supervised Learning.)

• Bold entries in the table mark constant attributes which cannot be changed from their default value.

• *: The environment is changing constantly over time in ContinualRLSetting, so there aren't really "tasks" to speak of.

Running experiments

--> (Reminder) First, take a look at the Examples <--

Directly in code:

from sequoia.settings import TaskIncrementalSLSetting
from sequoia.methods import BaseMethod
setting = TaskIncrementalSLSetting(dataset="mnist")
method = BaseMethod(max_epochs=1)

results = setting.apply(method)
print(results.summary())

From the command-line:

sequoia --setting <some_setting> --method <some_method>  (arguments)

For example:

• Run the BaseMethod on task-incremental MNIST, with one epoch per task, and without wandb:

  sequoia --setting task_incremental --dataset mnist --method baseline --max_epochs 1

• Run the PPO Method from stable-baselines3 on an incremental RL setting, with the default dataset (CartPole) and 5 tasks:

  sequoia --setting incremental_rl --nb_tasks 5 --method ppo --steps_per_task 10_000

• Running multiple experiments (wip):

  If you leave out the --method argument above, the experiment will compare the results of all the methods applicable to the chosen Setting.

  Likewise, if you leave the --setting option unset, the experiment will evaluate the performance of the selected method on all its applicable settings (WIP: and a table will be shown).