Reinforcement Learning Simulator Source Code Tutorial

Introduction

Welcome to the source code tutorial for the Reinforcement Learning (RL) Simulator! This guide will walk you through the core components and implementation details of the simulator, which is designed for training and testing RL algorithms in various environments.

Key Features

• Modular Architecture: Easy to extend and customize
• Support for Multiple Environments: Including grid worlds, continuous control, and custom scenarios
• Integration with RL Libraries: Compatible with TensorFlow, PyTorch, and OpenAI Gym

Getting Started

1. Clone the Repository

   git clone https://github.com/your-org/rl-simulator.git
   

2. Install Dependencies

   pip install -r requirements.txt
   

3. Run the Simulator

   python main.py
   

Core Code Structure

Here’s a breakdown of the critical modules:

1. Environment Initialization

import gym  
from simulator.envs import CustomEnv  

env = CustomEnv(config_file="config.yaml")  

2. Agent Definition

from agents.q_learning import QAgent  

agent = QAgent(state_space=env.observation_space, action_space=env.action_space)  

3. Training Loop

for episode in range(1000):  
    state = env.reset()  
    done = False  
    while not done:  
        action = agent.select_action(state)  
        next_state, reward, done, _ = env.step(action)  
        agent.update(state, action, reward, next_state)  
        state = next_state  

Expand Your Knowledge

For a quickstart guide to set up the simulator, visit /en/tech/ai/tutorials/rl_simulator/quickstart.

Additional Resources

• RL Algorithms Overview
• Custom Environment Setup
• Performance Optimization Tips

Let me know if you need further assistance! 🤖