Reinforcement Learning Projects

This repository contains implementations and simulations of various reinforcement learning algorithms and problems. Each project demonstrates different aspects of reinforcement learning, from basic concepts to advanced algorithms.

Repository Structure

├── src/                    # Source code for all projects
│   ├── common/            # Common utilities and shared code
│   └── projects/          # Individual project implementations
├── utils/                 # Utility scripts and tools
├── docs/                  # Documentation and resources
└── requirements.txt       # Project dependencies

Projects

1. N-Armed Bandit

Implementation of the N-armed bandit problem, demonstrating exploration vs exploitation trade-offs.

2. Gambler's Problem

Solution to the gambler's problem using value iteration and policy iteration methods.

3. Finding Optimum Direction

Implementation of algorithms for finding optimal directions in continuous spaces.

4. Prisoner's Dilemma

Simulation of the prisoner's dilemma using various reinforcement learning approaches.

Setup and Installation

1. Clone the repository:

git clone https://github.com/yourusername/Reinforcement-Learning.git
cd Reinforcement-Learning

2. Install dependencies:

pip install -r requirements.txt

Usage

Each project directory contains its own README with specific instructions for running the code and reproducing results.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.