A Beginner’s Guide to Reinforcement Learning Using Stable Baselines 3

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Stable Baselines 3 (SB3) is a popular library for reinforcement learning in Python, providing a collection of high-quality implementations of RL algorithms such as Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Deep Q-Network (DQN). In this tutorial, we will walk through the basics of using Stable Baselines 3 for training and evaluating RL agents.

Prerequisites

Before we begin, make sure you have Python 3.7 or later installed. You will also need to install the following libraries:

pip install stable-baselines3[extra] gym

Creating a Custom Gym Environment

To train an RL agent using Stable Baselines 3, we first need to create an environment that the agent can interact with. In this tutorial, we will use a simple example from the OpenAI Gym library called “CartPole-v1”:

import gym

env = gym.make("CartPole-v1")

Instantiating and Training an RL Agent

Now that we have our environment set up, we can instantiate an RL agent using an algorithm from the Stable Baselines 3 library. In this example, we will use the PPO algorithm:

from stable_baselines3 import PPO…