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running-experiments

Execute and analyze game-theoretic experiments using PPO and gradient methods.

Updated Mar 26, 2025
Search Tags:#data analysis#simulation#game theory#reinforcement learning#experimentation#PPO
One-click install
npx skills add https://github.com/GSU-FrankJ/tournament_experiment --skill running-experiments
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: running-experiments
Source: https://github.com/GSU-FrankJ/tournament_experiment/tree/main/.cursor/skills/running-experiments
Command: npx skills add https://github.com/GSU-FrankJ/tournament_experiment --skill running-experiments

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the execution and analysis of complex game theory experiments, allowing researchers and developers to efficiently test reinforcement learning agents and compare different algorithms.

Core Features & Use Cases

  • Experiment Execution: Run predefined experiment types (Two-Player Symmetric, Different Cost, Different Ability, Three Players) with configurable parameters.
  • Algorithm Comparison: Easily switch between PPO and gradient-based methods to compare their performance.
  • Parameter Sweeping: Configure and run experiments across various noise parameters (q) and custom game settings.
  • Analysis & Visualization: Facilitates the generation of convergence plots and detailed analysis of experiment results.
  • Use Case: You want to test how a PPO agent learns the Nash equilibrium in a two-player tournament. You can use this Skill to launch the experiment, monitor its convergence, and generate plots comparing it to a gradient baseline.

Quick Start

Run a PPO experiment for the two-player symmetric case with q=40 and a specific seed.

Frequently Asked Questions about running-experiments

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run game theory experiments with PPO and compare it to a gradient baseline?▼

You can run game theory experiments by executing predefined configurations for two-player or three-player tournaments, automatically comparing PPO against gradient methods to analyze agent performance and algorithmic convergence.

Can I automate parameter sweeps across different noise levels in reinforcement learning tournaments?▼

Yes, you can automate parameter sweeps by configuring experiments across various noise parameters (q) and custom game settings, enabling systematic evaluation of reinforcement learning agents under different tournament conditions.

What types of game theory tournaments can I simulate for reinforcement learning agents?▼

You can simulate two-player symmetric tournaments, different cost scenarios, different ability setups, and three-player tournaments, allowing comprehensive analysis of agent interactions and Nash equilibrium convergence.

How do I generate convergence plots for PPO agents in two-player tournaments?▼

You can generate convergence plots by running the experiment execution and utilizing the integrated analysis tools to visualize how PPO agents learn and converge toward equilibrium in symmetric and asymmetric game setups.

Do I need specific configuration files to test algorithm comparisons in game theory experiments?▼

Yes, you need to define configuration files to specify game parameters, noise values, and seeds, which the experiment runner uses to execute parameter sweeps and compare PPO with gradient-based methods.

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