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Co-authored-by: Omar Sanseviero <osanseviero@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

units/en/unit1/conclusion.mdx

@@ -1,14 +1,14 @@
 # Conclusion [[conclusion]]
 
-Congrats on finishing this chapter! **That was the biggest one**, and there was a lot of information. And congrats on finishing the tutorial. You’ve just trained your first Deep RL agents and shared it on the Hub 🥳.
+Congrats on finishing this unit! **That was the biggest one**, and there was a lot of information. And congrats on finishing the tutorial. You’ve just trained your first Deep RL agents and shared it with the community! 🥳
 
-That’s **normal if you still feel confused with all these elements**. This was the same for me and for all people who studied RL.
+It's **normal if you still feel confused with some of these elements**. This was the same for me and for all people who studied RL.
 
 **Take time to really grasp the material** before continuing. It’s important to master these elements and having a solid foundations before entering the fun part.
 
-Naturally, during the course, we’re going to use and explain these terms again, but it’s better to understand them before diving into the next chapters.
+Naturally, during the course, we’re going to use and explain these terms again, but it’s better to understand them before diving into the next units.
 
-In the next chapter, we’re going to reinforce what we just learn by **training Huggy the Dog to fetch the stick**.
+In the next (bonus) unit, we’re going to reinforce what we just learned by **training Huggy the Dog to fetch the stick**.
 
 You will be able then to play with him 🤗.
 

units/en/unit1/deep-rl.mdx

@@ -6,11 +6,11 @@ What we've talked about so far is Reinforcement Learning. But where does the "De
 
 Deep Reinforcement Learning introduces **deep neural networks to solve Reinforcement Learning problems** — hence the name “deep”.
 
-For instance, in the next article, we’ll work on Q-Learning (classic Reinforcement Learning) and then Deep Q-Learning both are value-based RL algorithms.
+For instance, in the next unit, we’ll learn about two value-based algorithms: Q-Learning (classic Reinforcement Learning) and then Deep Q-Learning.
 
 You’ll see the difference is that in the first approach, **we use a traditional algorithm** to create a Q table that helps us find what action to take for each state.
 
-In the second approach, **we will use a Neural Network** (to approximate the q value).
+In the second approach, **we will use a Neural Network** (to approximate the Q value).
 
 <figure>
 <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/deep.jpg" alt="Value based RL"/>
@@ -18,4 +18,4 @@ In the second approach, **we will use a Neural Network** (to approximate the q
 </figcaption>
 </figure>
 
-If you are not familiar with Deep Learning you definitely should watch <a href="[https://course.fast.ai/](https://course.fast.ai/)">the fastai Practical Deep Learning for Coders (Free)</a>
+If you are not familiar with Deep Learning you definitely should watch [the FastAI Practical Deep Learning for Coders](https://course.fast.a) (Free).

units/en/unit1/exp-exp-tradeoff.mdx

@@ -1,4 +1,4 @@
-# The Exploration/ Exploitation tradeoff [[exp-exp-tradeoff]]
+# The Exploration/Exploitation trade-off [[exp-exp-tradeoff]]
 
 Finally, before looking at the different methods to solve Reinforcement Learning problems, we must cover one more very important topic: *the exploration/exploitation trade-off.*
 
@@ -19,9 +19,10 @@ But if our agent does a little bit of exploration, it can **discover the big re
 
 This is what we call the exploration/exploitation trade-off. We need to balance how much we **explore the environment** and how much we **exploit what we know about the environment.**
 
-Therefore, we must **define a rule that helps to handle this trade-off**. We’ll see the different ways to handle it in the future chapters.
+Therefore, we must **define a rule that helps to handle this trade-off**. We’ll see the different ways to handle it in the future units.
+
+If it’s still confusing, **think of a real problem: the choice of picking a restaurant:**
 
-If it’s still confusing, **think of a real problem: the choice of a restaurant:**
 
 <figure>
 <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/exp_2.jpg" alt="Exploration">

units/en/unit1/hands-on.mdx

@@ -8,6 +8,6 @@ A Lunar Lander agent that will learn to land correctly on the Moon 🌕
 
 And finally, you'll **upload this trained agent to the Hugging Face Hub 🤗, a free, open platform where people can share ML models, datasets, and demos.**
 
-Thanks to our <a href="https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard">leaderboard</a>, you'll be able to compare your results with other classmates and exchange the best practices to improve your agent's scores Who will win the challenge for Unit 1 🏆?
+Thanks to our <a href="https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard">leaderboard</a>, you'll be able to compare your results with other classmates and exchange the best practices to improve your agent's scores. Who will win the challenge for Unit 1 🏆?
 
 So let's get started! 🚀

units/en/unit1/introduction.mdx

@@ -7,9 +7,11 @@ Welcome to the most fascinating topic in Artificial Intelligence: **Deep Reinfo
 
 Deep RL is a type of Machine Learning where an agent learns **how to behave** in an environment **by performing actions** and **seeing the results.**
 
-So in this first chapter, **you'll learn the foundations of Deep Reinforcement Learning.**
+In this first unit, **you'll learn the foundations of Deep Reinforcement Learning.**
+
+
+Then, you'll **train your Deep Reinforcement Learning agent, a lunar lander to land correctly on the Moon** using <a href="https://stable-baselines3.readthedocs.io/en/master/"> Stable-Baselines3 </a>, a Deep Reinforcement Learning library.
 
-Then, you'll **train your Deep Reinforcement Learning agent, a lunar lander to land correctly on the Moon** using <a href="https://stable-baselines3.readthedocs.io/en/master/"> Stable-Baselines3 </a> a Deep Reinforcement Learning library.
 
 <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/lunarLander.gif" alt="LunarLander">
 

units/en/unit1/quiz.mdx

@@ -132,7 +132,7 @@ In Reinforcement Learning, we need to **balance how much we explore the environm
 <details>
 <summary>Solution</summary>
 
-- The Policy π **is the brain of our Agent**, it’s the function that tell us what action to take given the state we are. So it defines the agent’s behavior at a given time.
+- The Policy π **is the brain of our Agent**. It’s the function that tells us what action to take given the state we are in. So it defines the agent’s behavior at a given time.
 
 <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/policy_1.jpg" alt="Policy">
 

units/en/unit1/rl-framework.mdx

@@ -23,7 +23,7 @@ This RL loop outputs a sequence of **state, action, reward and next state.**
 
 <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/sars.jpg" alt="State, Action, Reward, Next State" width="100%">
 
-The agent's goal is to maximize its cumulative reward, **called the expected return.**
+The agent's goal is to _maximize_ its cumulative reward, **called the expected return.**
 
 ## The reward hypothesis: the central idea of Reinforcement Learning [[reward-hypothesis]]
 
@@ -31,19 +31,20 @@ The agent's goal is to maximize its cumulative reward, **called the expected re
 
 Because RL is based on the **reward hypothesis**, which is that all goals can be described as the **maximization of the expected return** (expected cumulative reward).
 
-That’s why in Reinforcement Learning, **to have the best behavior,** we need to **maximize the expected cumulative reward.**
+That’s why in Reinforcement Learning, **to have the best behavior,** we aim to learn to take actions that **maximize the expected cumulative reward.**
+
 
 ## Markov Property [[markov-property]]
 
 In papers, you’ll see that the RL process is called the **Markov Decision Process** (MDP).
 
-We’ll talk again about the Markov Property in the following units. But if you need to remember something today about it, Markov Property implies that our agent needs **only the current state to decide** what action to take and **not the history of all the states** **and actions** they took before.
+We’ll talk again about the Markov Property in the following units. But if you need to remember something today about it, it's this: the Markov Property implies that our agent needs **only the current state to decide** what action to take and **not the history of all the states and actions** they took before.
 
 ## Observations/States Space [[obs-space]]
 
 Observations/States are the **information our agent gets from the environment.** In the case of a video game, it can be a frame (a screenshot). In the case of the trading agent, it can be the value of a certain stock, etc.
 
-There is a differentiation to make between *observation* and *state*:
+There is a differentiation to make between *observation* and *state*, however:
 
 - *State s*: is **a complete description of the state of the world** (there is no hidden information). In a fully observed environment.
 
@@ -53,9 +54,7 @@ There is a differentiation to make between *observation* and *state*:
 <figcaption>In chess game, we receive a state from the environment since we have access to the whole check board information.</figcaption>
 </figure>
 
-In chess game, we receive a state from the environment since we have access to the whole check board information.
-
-With a chess game, we are in a fully observed environment, since we have access to the whole check board information.
+In a chess game, we have access to the whole board information, so we receive a state from the environment. In other words, the environment is fully observed.
 
 - *Observation o*: is a **partial description of the state.** In a partially observed environment.
 
@@ -69,7 +68,7 @@ In Super Mario Bros, we only see a part of the level close to the player, so we
 In Super Mario Bros, we are in a partially observed environment. We receive an observation **since we only see a part of the level.**
 
 <Tip>
-In reality, we use the term state in this course but we will make the distinction in implementations.
+In this course, we use the term "state" to denote both state and observation, but we will make the distinction in implementations.
 </Tip>
 
 To recap:
@@ -86,7 +85,8 @@ The actions can come from a *discrete* or *continuous space*:
 
 <figure>
 <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/mario.jpg" alt="Mario">
-<figcaption>Again, in Super Mario Bros, we have only 4 directions and jump possible</figcaption>
+<figcaption>Again, in Super Mario Bros, we have only 5 possible actions: 4 directions and jumping</figcaption>
+
 </figure>
 
 In Super Mario Bros, we have a finite set of actions since we have only 4 directions and jump.