README.md

metadata

title: Gym Campaign Slogan Comparator Using RL
emoji: 🐠
colorFrom: pink
colorTo: indigo
sdk: gradio
sdk_version: 5.42.0
app_file: app.py
pinned: false
short_description: Compares the effectiveness of slogans generated using RL.

Gym Campaign Slogan Comparator

This application compares the effectiveness of slogans generated by two models for a gym campaign. Each model generates slogans along with a predicted effectiveness score. The app calculates the success rate of each model by comparing their slogans pairwise.

Features

• Compare two lists of slogans with their effectiveness scores.
• Determine which model performs better overall.
• Visualize the success rate of each model in a simple interface.
• Shareable Gradio app for easy use.

Inputs

• Model X Slogans: A list of slogans with scores (format: slogan,score per line).
• Model Y Slogans: Same format for the second model.

Outputs

• Model X Success Rate: Percentage of pairwise wins for Model X.
• Model Y Success Rate: Percentage of pairwise wins for Model Y.

Reinforcement Learning Context

Reinforcement Learning (RL) can be applied to optimize slogan generation over time. In this app:

• Each slogan generation by the models is considered an action.
• The effectiveness score acts as a reward signal.
• By comparing slogans and updating the model to maximize wins (success rate), RL techniques like policy gradients can be used to improve the quality of slogans over iterations.

This app provides a simple interface to visualize these comparisons and can serve as a foundation for RL-based optimization of advertising campaigns.