This repository delivers a Python-based framework implementing multiple AI agents to play Rock-Paper-Scissors, including random, heuristic, and Q-learning strategies. It enables users to compare performance metrics, visualize results, and extend with custom agent implementations for education and research.
AI Platforms & Frameworks
Education
Gaming
#AI strategies
#Rock Paper Scissors
#open-source project
#reinforcement learning
#pattern recognition
#Game Development
#Python AI
#competitive AI
#learning parameters
#modular design
#performance visualization
#AI education
#game runner
#AI training
#Q-Learning
#agent swapping
#Data Logging
#Easy Setup
#game behavior
#custom extensions
...
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AI Agents for Rock Paper Scissors
This repository delivers a Python-based framework implementing multiple AI agents to play Rock-Paper-Scissors, including random, heuristic, and Q-learning strategies. It enables users to compare performance metrics, visualize results, and extend with custom agent implementations for education and research.
AI Platforms & Frameworks
Education
Gaming
#AI strategies
#Rock Paper Scissors
#open-source project
#reinforcement learning
#pattern recognition
#Game Development
#Python AI
#competitive AI
#learning parameters
#modular design
#performance visualization
#AI education
#game runner
#AI training
#Q-Learning
#agent swapping
#Data Logging
#Easy Setup
#game behavior
#custom extensions
...
Featured
What is AI Agents for Rock Paper Scissors?
AI Agents for Rock Paper Scissors is an open-source Python project that demonstrates how to build, train, and evaluate different AI strategies—random play, rule-based pattern recognition, and reinforcement learning (Q-learning)—in the classic Rock-Paper-Scissors game. It provides modular agent classes, a configurable game runner, performance logging, and visualization utilities. Users can easily swap agents, adjust learning parameters, and explore AI behavior in competitive scenarios.
Who will use AI Agents for Rock Paper Scissors?
- Students learning AI and reinforcement learning
- AI researchers experimenting with simple game environments
- Educators creating hands-on tutorials
- Hobbyists exploring AI strategies
How to use the AI Agents for Rock Paper Scissors?
- Step1: Clone the repository: git clone https://github.com/bnurbekov/AI_Agents_For_Rock_Paper_Scissors.git
- Step2: Navigate into the folder: cd AI_Agents_For_Rock_Paper_Scissors
- Step3: Install dependencies: pip install -r requirements.txt
- Step4: Choose or implement an agent in the agents/ directory
- Step5: Run the game: python main.py --agent1 RandomAgent --agent2 QLearningAgent
- Step6: View performance logs and result plots in the outputs/ folder
Platform
- mac
- windows
- linux
AI Agents for Rock Paper Scissors's Core Features & Benefits
The Core Features
- Random play agent
- Rule-based pattern recognition agent
- Q-learning reinforcement learning agent
- Configurable game runner
- Performance logging and visualization
The Benefits
- Easy to set up and run
- Modular design for custom extensions
- Educational insights into AI strategies
- Open-source and free to use
AI Agents for Rock Paper Scissors's Main Use Cases & Applications
- Teaching basic reinforcement learning concepts
- Comparing AI strategy performance in simple games
- Prototyping custom game-playing agents
- Demonstrating AI behavior in competitive scenarios
FAQs of AI Agents for Rock Paper Scissors
AI Agents for Rock Paper Scissors Company Information
AI Agents for Rock Paper Scissors Reviews
5/5
AI Agents for Rock Paper Scissors's Main Competitors and alternatives?
- OpenAI Gym Rock-Paper-Scissors environment
- RLCard library for card and simple games
- Custom Tic-Tac-Toe AI frameworks
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