Welcome to the very first lesson of "Environment Engineering: The Foundation of RL Systems"! This course is part of our "Playing Games with Reinforcement Learning" path, where you'll embark on an exciting journey to master Reinforcement Learning by building intelligent agents that can autonomously navigate and learn in complex environments.

Throughout this learning journey, you'll build your skills step by step - first mastering how to create environments where agents can learn, then discovering how Q-Learning enables these agents to make intelligent decisions. You'll then bring everything together by connecting your smart agents with the environments you've built, before finally exploring techniques to optimize their performance. By the time you complete this path, you'll be equipped to build complete reinforcement learning systems that can tackle challenging problems and even learn to master games on their own!

Today, we'll begin with the very foundations of Reinforcement Learning (RL) — understanding what it is and the core components that make RL systems work. Let's dive in together!

Reinforcement Learning is a subset of Machine Learning where an agent learns to make decisions by interacting with an environment. Unlike Supervised Learning (where we train with labeled examples) or Unsupervised Learning (which finds patterns in unlabeled data), Reinforcement Learning is about learning through trial and error and receiving feedback. Think about how you learned to ride a bicycle. Nobody explicitly told you the exact angle to turn the handlebar or how much pressure to apply to the pedals. Instead, you tried different approaches, fell a few times, and gradually improved by getting feedback (staying upright felt good, falling hurt!). This process of learning from experience is the essence of Reinforcement Learning.

The RL setting involves several components:

• An agent that makes decisions;
• An environment the agent interacts with;
• States that represent the situation of the environment;
• Actions the agent can take in each state;
• Rewards that provide feedback on how good the actions were.

Here's a common visual representation of the so-called "RL loop" that comprises the above components:

A classic example that we'll explore throughout this course is the Grid World environment. Imagine a robot navigating through a 4×4 grid trying to reach a goal. When the robot takes an action (like moving right), it transitions from its current state to a new state according to the rules of the environment. The robot must learn which sequences of actions lead to the goal most efficiently by receiving rewards when it reaches the destination. This seemingly simple example will help us understand the core principles that power even the most complex RL systems.

Here's how you can visualize this 4x4 grid world:

In this visualization, A represents the (starting) position of the agent, while G represents the goal position the agent is trying to reach. Each cell in the grid is a possible state the agent can be in.

In Reinforcement Learning, a state represents the current situation of the environment. It contains all the information your agent needs to make a decision. Let's exemplify this with different scenarios:

• In our grid world, a state is simply the position of the agent, represented as coordinates (row, column).
• In a chess game, a state is the position of all pieces on the board.
• For a self-driving car, a state might include its position, speed, direction, and information about nearby objects.
• In a stock trading RL system, a state could represent market conditions, price history, and portfolio status.

The key to designing effective states is balance — you want to capture essential information while avoiding unnecessary details that might slow down your agent's learning.

Here's how we can define states for our 4×4 grid world:

When you run this code, it creates 16 possible states representing each cell in our 4×4 grid. For example, (0, 0) represents the top-left corner, while (3, 3) represents the bottom-right corner.

Have you ever played a video game where the character can be in different positions? Those positions are essentially the states of the game! In more complex environments, states might be represented as feature vectors, images, or even complex data structures that capture all relevant information about the current situation.

Actions are the choices your agent can make in a given state. Think about a remote control: each button represents a different action you can take to control a device; similarly, your RL agent has a set of "buttons" (actions) it can press to interact with its environment. Let's see some examples of possible action spaces in different environments:

• In our grid world, actions are movement directions: up, down, left, right.
• In a video game like Super Mario, actions might be button presses: jump, run, move left/right.
• For a robot arm, actions could be joint movements or target positions.
• In a recommender system (like Netflix or YouTube), actions might be different items to recommend to a user.

When designing your action space, you'll want to provide enough flexibility for your agent to solve the problem while keeping it constrained enough to make learning feasible. Too many actions can make learning extremely slow!

Let's define the actions for our grid world:

In this code, we've represented actions as integers 0-3 for computational efficiency, but we've also created a dictionary that maps these numbers to human-readable meanings. This makes our code both efficient and understandable — a best practice you'll see throughout RL systems.

Let's visualize this, in the following grid world we're again using `

Transitions are the rules that govern how an environment changes when an agent takes an action in a given state. They form the backbone of the environment's dynamics, determining what happens next after each decision. Think of transitions like the laws of physics in your virtual world — they define what's possible and how things change.

Let's code a simple transition function for our grid world:

This function implements a deterministic transition model for our grid world. Notice how we use max and min to prevent the agent from moving outside the grid boundaries — these constraints are part of our environment's rules. If the agent attempts to move into a wall or off the grid, it simply stays in place along that dimension.

Let's visualize this, again in the following we're using A to represent the agent's position, G for the goal, and numbers represent the possible actions:

Have you ever noticed in video games how characters can't walk through walls? That's a transition rule! In more complex environments, transitions might involve sophisticated physics simulations or intricate rule systems, but the fundamental concept remains the same: they define how the world responds to an agent's actions.

The reward function is the heart and soul of Reinforcement Learning. It provides the feedback that guides your agent's learning process, signaling what outcomes are desirable or undesirable. Think about how we train pets — we give them treats when they perform the desired behavior. The reward function works the same way, but instead of treats, we're giving numerical values that guide the learning process.

As the famous saying in RL goes: "You get what you reward, not what you want." This highlights how critical it is to design your reward function carefully. A poorly designed reward can lead to surprising and unintended agent behaviors!

Let's explore some reward function examples:

• In our grid world, we might give a positive reward for reaching the goal and zero elsewhere.
• Similarly, in a game like chess, rewards might come only at the end (1 for winning, -1 for losing, and 0 for draw).
• For a self-driving car, rewards could penalize dangerous maneuvers and reward smooth, efficient driving.
• For a data center cooling system, rewards could be tied to maintaining optimal temperatures while penalizing excessive energy consumption.

Now, let's define a simple reward function for our grid world:

This reward function gives your agent a reward of 1.0 when it reaches the goal state (3, 3) and 0.0 in all other states. Each time the agent takes an action and moves to a new state, the environment would call this function to determine what reward to provide, forming the crucial feedback signal that drives learning.

More sophisticated reward functions might include additional components, such as small positive rewards for moving closer to the goal (to incentivize good behaviour that leads to the goal), but also penalties. A penalty can be thought of as a negative reward. For example, we might give a small penalty for every step taken, to encourage learning a behaviour that gets the agent to the goal as fast as possible, or we might give a large penalty for visiting certain "dangerous" states we don't want the agent to visit.

Designing effective reward functions is both an art and a science — one you'll get better at with practice!

Congratulations! You've just completed the first lesson in our "Environment Engineering" course. We've covered the fundamental building blocks of any Reinforcement Learning system, such as states that represent the environment situation, actions that define what the agent can do, and reward functions that provide the learning signal.

These concepts form the foundation upon which all Reinforcement Learning systems are built. By understanding these components, you're now prepared to start implementing your own RL environments.

In the upcoming practice exercises, you'll get hands-on experience working with these concepts. You'll define states, actions, and rewards for various scenarios, and see how these components work together in a Reinforcement Learning framework.

As we progress through this course, we'll build upon these foundations to create increasingly sophisticated environments and develop agents that can learn to navigate them effectively. You're taking the first steps on an exciting journey toward mastering Reinforcement Learning!

Get ready to apply what you've learned and solidify your understanding through practical coding exercises. Remember, Reinforcement Learning is a field where practice and experimentation are key to developing intuition and mastery!