Welcome to our tutorial on the Reinforcement Learning Simulator! In this guide, we'll walk you through the basics of using the simulator and how to get started with your own RL projects.

Key Features

  • Interactive Environment: Simulate various scenarios with a user-friendly interface.
  • Customizable Rewards: Design your own reward systems to test different learning algorithms.
  • Visualize Learning: Track and visualize the learning process in real-time.

Getting Started

  1. Download and Install: Download the simulator and follow the installation guide.
  2. Create a New Project: Open the simulator and start a new project.
  3. Define the Environment: Set up the environment with the desired parameters.
  4. Train Your Agent: Choose a learning algorithm and start training your agent.
  5. Evaluate Performance: Test the performance of your agent in the simulated environment.

Example Scenario

Imagine you're training an agent to navigate a maze. Here's a breakdown of the steps:

  1. Define the Maze: Create a maze environment with walls and a goal position.
  2. Set Rewards: Define positive rewards for reaching the goal and negative rewards for hitting walls.
  3. Choose an Algorithm: Select a learning algorithm like Q-learning or SARSA.
  4. Train the Agent: Run the simulation and let the agent learn.
  5. Analyze Results: Visualize the learning process and evaluate the agent's performance.

Maze Example

Further Reading

By following this tutorial, you'll gain a solid understanding of how to use the Reinforcement Learning Simulator and start your own projects. Happy learning! 🎓