https://github.com/sukiboo/sxf

Synthetic Experimental Framework for hyper-personalized behavioral nudging
https://github.com/sukiboo/sxf

experimental-design nudging reinforcement-learning simulated-environments tensorflow

Last synced: about 2 months ago
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Synthetic Experimental Framework for hyper-personalized behavioral nudging

• Host: GitHub
• URL: https://github.com/sukiboo/sxf
• Owner: sukiboo
• License: mit
• Created: 2024-02-18T21:02:31.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-02-22T15:14:41.000Z (over 2 years ago)
• Last Synced: 2025-01-21T12:35:52.539Z (over 1 year ago)
• Topics: experimental-design, nudging, reinforcement-learning, simulated-environments, tensorflow
• Language: Python
• Homepage:
• Size: 19.5 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Synthetic Experimental Framework

This repository includes a customizable simulated environment for testing different aspects of the agent/environment interaction in addressing the behavioral nudging personalization contextual bandit problem. The code was written around 2022 and may be a bit outdated now.

The findings from SXF contributed to the papers

- [Examining Policy Entropy of Reinforcement Learning Agents for Personalization Tasks](https://arxiv.org/abs/2211.11869)

- [Increasing Entropy to Boost Policy Gradient Performance on Personalization Tasks](https://arxiv.org/abs/2310.05324)

- [On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks](https://arxiv.org/abs/2112.13141)

![exp_action_dist](https://github.com/sukiboo/sxf/assets/38059493/931978ba-2d8c-43f4-b3de-01704f24f358)

## Installation

* Install conda / pip requirements via `conda env create -f environment.yml`

* Activate conda environment with `conda activate synthetic-experimental-framework`

* Modify experiment configuration in `configs/config.yml` as needed

* Run a new experiment via `python -m run_experiment`, or load a recorded experiment `exp_name` via `python -m run_experiment --load exp_name`

* Results of the experiment can be found in `exp_data/exp_name/` directory

## File Overview

* `environment.yml` --- list of the required packages

* `configs/config.yml` --- config file containing the experiment/envirnment/agent parameters

* `run_experiment.py` --- main module to run the experiment

* `exp_data/` --- directory containing the experiment data, images, checkpoints

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* `experiment_component/experiment.py` --- setup the experiment

* `experiment_component/data_visualization.py` --- compute and report results of an experiment

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* `environment_component/environment.py` --- setup the environment

* `environment_component/state_space.py` --- setup the state space for the environment

* `environment_component/action_space.py` --- setup the action space for the environment

* `environment_component/reward_function.py` --- setup the reward function for the environment

* `environment_component/feedback_signal.py` --- setup the feedback signal that is given to the agent

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* `agent_component/agent.py` --- setup the agent

* `agent_component/network_architecture.py` --- setup the policy for the agent

* `agent_component/loss_function.py` --- setup the loss function for the agent