Beginner RL Tutorial

Welcome to the beginner's tutorial on reinforcement learning! This guide will take you through the basics of reinforcement learning, helping you understand the concepts and techniques used in this exciting field.

What is Reinforcement Learning?

Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize some notion of cumulative reward. Unlike supervised learning, where the agent is trained on labeled data, in reinforcement learning, the agent learns through trial and error.

Key Components

• Agent: The decision-making entity in the environment.
• Environment: The environment in which the agent operates, providing feedback in the form of rewards and states.
• State: The current situation of the agent within the environment.
• Action: The action taken by the agent in response to the current state.
• Reward: The feedback received by the agent after taking an action.

Getting Started

To get started with reinforcement learning, you can install OpenAI's Gym library, which provides a set of pre-defined environments for training and testing your agents.

pip install gym

Once you have installed Gym, you can try the following example to get a feel for the environment:

import gym
import random

env = gym.make('CartPole-v0')
for _ in range(100):
    state = env.reset()
    for _ in range(500):
        action = random.randrange(2)
        state, reward, done, _ = env.step(action)
        if done:
            break
    env.close()

Resources

For further reading and resources, you can check out the following links:

• OpenAI Gym Documentation
• Reinforcement Learning: An Introduction

Images

Here's a visual representation of a reinforcement learning environment:

We hope this tutorial has given you a good starting point for your journey into reinforcement learning. Happy learning!