CartPole is a classic control problem in reinforcement learning. In this project, you will learn how to implement the CartPole problem using Python.

Overview

CartPole is a simple physics-based environment where a pole is attached to a cart that can move left and right. The goal is to keep the pole balanced without it falling over. This project will guide you through the steps of implementing the CartPole problem from scratch.

Installation

Before you start, make sure you have the following installed:

  • Python 3.x
  • NumPy
  • Matplotlib

You can install NumPy and Matplotlib using pip:

pip install numpy matplotlib

Environment Setup

To simulate the CartPole environment, we will use the OpenAI Gym library. Install it using pip:

pip install gym

Implementation Steps

  1. Initialize the Environment: Create an instance of the CartPole environment.
  2. Define the Policy: Implement a simple policy that can control the cart and the pole.
  3. Train the Policy: Use reinforcement learning techniques to train the policy.
  4. Evaluate the Policy: Test the policy's performance on the trained environment.

Code Example

Here is a simple code example to get you started:

import gym
import numpy as np

# Initialize the environment
env = gym.make('CartPole-v1')

# Define the policy
def policy(state):
    action = 0  # Random action
    return action

# Train the policy
for _ in range(1000):
    state = env.reset()
    while True:
        action = policy(state)
        next_state, reward, done, _ = env.step(action)
        if done:
            break
        state = next_state

# Evaluate the policy
state = env.reset()
while True:
    action = policy(state)
    next_state, reward, done, _ = env.step(action)
    if done:
        break
    state = next_state

Further Reading

For more information on CartPole and reinforcement learning, check out the following resources:

Images

Here is an image of the CartPole environment:

CartPole