1. Introduction

In this tutorial, we will guide you through the setup of an environment for Reinforcement Learning (RL). By setting up different scenarios or conditions, we can change the way an AI agent operates. The goal is to give you a solid understanding of how to set up and customize environments for your specific RL tasks.

By the end of the tutorial, you will be able to:
- Understand the concept of RL environments
- Set up a custom environment for RL using OpenAI Gym
- Test and use the environment to train an RL agent

Prerequisites:
- Basic understanding of Python programming
- Familiarity with Reinforcement Learning concepts
- Python (3.6 or higher) installed on your machine

2. Step-by-Step Guide

2.1 Understanding RL Environments

An RL environment is the world through which an agent moves, taking actions and getting rewarded based on those actions. It defines the conditions under which the agent operates.

2.2 Setting Up OpenAI Gym

OpenAI Gym is a popular Python library for developing and comparing RL algorithms. It comes with several pre-defined environments we can use, or you can create your own.

Installation is simple. Just run the following command on your terminal:

pip install gym

2.3 Creating Your Own Environment

Creating your own environment involves defining the states, actions, and rewards for your specific task.

3. Code Examples

3.1 Creating a Custom Environment

Here's a simple example of a custom environment. This environment will have two states and two possible actions.

import gym
from gym import spaces

class CustomEnv(gym.Env):
    def __init__(self):
        self.state = 0
        self.action_space = spaces.Discrete(2)
        self.observation_space = spaces.Discrete(2)

    def step(self, action):
        if action == 1:
            self.state = 1 - self.state
        return self.state, 0, False, {}

    def reset(self):
        self.state = 0
        return self.state

In this code:
- We define a class CustomEnv that extends gym.Env.
- self.action_space is the space of possible actions. spaces.Discrete(2) means there are two possible actions: 0 and 1.
- self.observation_space is the space of possible states.
- step is the function that takes an action and returns the new state, reward, done (whether the episode is finished), and info (extra information which can be useful for debugging).
- reset is the function that resets the environment to its initial state.

3.2 Using the Environment

Once you've created your environment, you can use it to train an agent. Here's a simple example:

env = CustomEnv()

for i_episode in range(20):
    observation = env.reset()
    for t in range(100):
        action = env.action_space.sample()  # choose a random action
        observation, reward, done, info = env.step(action)

In this code, we create an instance of our custom environment. We then run 20 episodes, each with up to 100 time steps. At each time step, we randomly choose an action and apply it to the environment.

4. Summary

In this tutorial, we covered the basics of setting up an environment for Reinforcement Learning. We looked at how to create a custom environment using OpenAI Gym, defining the possible states and actions. We also discussed how to use the environment to run episodes and interact with the environment.

Next steps for learning could include looking at more complex environments, and how to define more complex actions and rewards. For additional resources, check out the OpenAI Gym documentation.

5. Practice Exercises

1. Modify the CustomEnv class to add a reward for action 1 when in state 0, and a penalty for action 1 when in state 1.
2. Create a new environment with more than two states and actions.
3. Write code to run 100 episodes in your new environment, and keep track of the total reward for each episode.

Remember, the best way to learn is by doing. Happy coding!