Welcome to the Reinforcement Learning (RL) Simulator API documentation! 🚀 This guide will help you understand how to interact with the RL Simulator API to build and test your reinforcement learning algorithms.

Key Features

  • Environment Interaction: Control simulation parameters and agent behavior
  • Training Metrics: Access real-time reward, episode stats, and convergence data
  • Scenario Customization: Define custom environments and reward functions
  • Visualization Tools: Generate interactive plots of training progress
Reinforcement_Learning_Simulator

Quick Start Example

import rl_simulator

# Initialize simulator
sim = rl_simulator.Simulator(environment="CartPole-v1", render_mode="human")

# Train agent
sim.train(epochs=100, learning_rate=0.001)

# Get results
print(sim.get_metrics())  # Output: {"total_reward": 498, "success_rate": 0.92}

API Endpoints

Endpoint Description Example
/api/simulate Start a new simulation POST /api/simulate?env=MountainCar
/api/train Train the agent GET /api/train?algorithm=Q-Learning
/api/analyze Retrieve performance analysis GET /api/analyze?format=json
API_Simulation_Flow

Need Help?

If you're new to RL simulators, check out our Quick Start Guide for hands-on examples. 📚
For advanced customization options, explore the RL Simulator Configuration Docs. 🔧

Let me know if you'd like to dive deeper into specific API functionalities! 🌐