Welcome to the Reinforcement Learning (RL) Simulator Quickstart! 🚀 This tutorial will walk you through setting up and running your first RL simulation using our platform.

📚 What is RL Simulator?

Reinforcement Learning Simulators are tools that allow you to train agents in virtual environments. They are essential for experimenting with algorithms like Q-learning, Deep Q-Networks (DQN), and Policy Gradients.

reinforcement_learning

🛠 Installation

  1. Clone the repository: 
    git clone https://github.com/yourorg/rl-simulator.git
  2. Install dependencies: 
    pip install -r requirements.txt
  3. Start the simulator: 
    python simulator.py
rl_simulator_installation

🧠 Basic Usage

To begin, create a simple environment and agent: 

from rl_simulator import Environment, Agent

env = Environment("CartPole-v1")
agent = Agent(env, learning_rate=0.01)
agent.train(epochs=100)
code_example

🌐 Expand Your Knowledge

For deeper insights into RL concepts, check out our Advanced Topics guide.

Let me know if you need help with custom environments or algorithm tuning! 💡