Introduction

Welcome to this tutorial on Reinforcement Learning! Our goal is to help beginners get familiar with the basics of Reinforcement Learning, a branch of Machine Learning.

By the end of this tutorial, you will:

• Understand the basic concepts of Reinforcement Learning
• Learn how to set up a basic Reinforcement Learning model
• Apply learned concepts to some simple practical examples

Prerequisites: Some basic understanding of Python and Machine Learning is recommended.

Step-by-Step Guide

What is Reinforcement Learning?

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with its environment. In RL, an agent takes actions based on its current state and receives feedback in the form of rewards or penalties. The goal of the agent is to learn the optimal policy, i.e., a sequence of actions that maximizes the total reward over time.

Key Concepts of Reinforcement Learning

1. Agent: The decision-maker or the learner.
2. Environment: The world through which the agent moves.
3. Action (A): What the agent can do.
4. State (S): The current situation returned by the environment.
5. Reward (R): An immediate return sent back from the environment to evaluate the last action.
6. Policy (π): The strategy that the agent employs to determine the next action based on the current state.
7. Value (V): The expected long-term return with discount, as opposed to the short-term reward R.

Reinforcement Learning Process

The process begins with the agent observing the environment. Based on the observed state, the agent takes an action. The environment transitions to a new state and returns a reward to the agent. The agent updates its knowledge with the new experience and repeats the process.

Code Examples

We will create a simple reinforcement learning model using Python and the OpenAI Gym library. The 'FrozenLake-v0' environment in Gym is a great starting point for beginners. In this environment, the agent controls the movement of a character in a grid world.

Step 1: Import Required Libraries

import gym  # OpenAI Gym library
import numpy as np  # For numerical operations

Step 2: Create the Environment

# Create the FrozenLake environment
env = gym.make('FrozenLake-v0')

Step 3: Initialize the Q-Table

# Initialize Q-table with zeros
Q = np.zeros([env.observation_space.n, env.action_space.n])

Step 4: Implement the Learning Algorithm

# Set learning parameters
lr = .8
y = .95
num_episodes = 2000
rList = []

for i in range(num_episodes):
    # Reset state
    s = env.reset()
    rAll = 0
    d = False
    j = 0
    # The Q-Table learning algorithm
    while j < 99:
        j+=1
        # Choose action from Q table
        a = np.argmax(Q[s,:] + np.random.randn(1,env.action_space.n)*(1./(i+1)))
        # Get new state & reward from environment
        s1,r,d,_ = env.step(a)
        # Update Q-Table with new knowledge
        Q[s,a] = Q[s,a] + lr*(r + y*np.max(Q[s1,:]) - Q[s,a])
        rAll += r
        s = s1
        if d == True:
            break
    rList.append(rAll)

Summary

In this tutorial, we introduced Reinforcement Learning and its key concepts. We also implemented a simple RL model using Python and OpenAI Gym. The next steps would be to explore more complex environments and reinforcement learning algorithms.

Additional resources:

• The Reinforcement Learning book by Richard S. Sutton and Andrew G. Barto
• OpenAI Gym's documentation: https://gym.openai.com/docs/

Practice Exercises

1. Exercise 1: Try to implement a similar reinforcement learning model on a different gym environment.
2. Exercise 2: Modify the learning parameters (like learning rate, discount factor) and observe how they affect the model's performance.
3. Exercise 3: Implement a different reinforcement learning algorithm (like SARSA or Monte Carlo methods) on the same environment.

Solutions and explanations for these exercises can be found in the official Gym documentation and Reinforcement Learning book recommended above. Further practice can be done through implementing RL models on various environments and with different learning algorithms.