From 9479050de0b01c7576f638446afd6fb0a3cdbc0d Mon Sep 17 00:00:00 2001
From: Maxim Bonnaerens <maxim@bonnaerens.be>
Date: Thu, 26 Jan 2023 11:34:06 +0100
Subject: [PATCH] Minor text fixes

---
 units/en/unit1/tasks.mdx       | 2 +-
 units/en/unit1/two-methods.mdx | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/units/en/unit1/tasks.mdx b/units/en/unit1/tasks.mdx
index 1be4fea..9eb83a2 100644
--- a/units/en/unit1/tasks.mdx
+++ b/units/en/unit1/tasks.mdx
@@ -17,7 +17,7 @@ For instance, think about Super Mario Bros: an episode begin at the launch of a
 
 ## Continuing tasks [[continuing-tasks]]
 
-These are tasks that continue forever (no terminal state). In this case, the agent must **learn how to choose the best actions and simultaneously interact with the environment.**
+These are tasks that continue forever (**no terminal state**). In this case, the agent must **learn how to choose the best actions and simultaneously interact with the environment.**
 
 For instance, an agent that does automated stock trading. For this task, there is no starting point and terminal state. **The agent keeps running until we decide to stop it.**
 
diff --git a/units/en/unit1/two-methods.mdx b/units/en/unit1/two-methods.mdx
index e6459c2..5818e5e 100644
--- a/units/en/unit1/two-methods.mdx
+++ b/units/en/unit1/two-methods.mdx
@@ -8,7 +8,7 @@ In other terms, how to build an RL agent that can **select the actions that ma
 
 ## The Policy π: the agent’s brain [[policy]]
 
-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.** 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.
 
 <figure>
 <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/policy_1.jpg" alt="Policy" />
@@ -67,7 +67,7 @@ If we recap:
 
 ## Value-based methods [[value-based]]
 
-In value-based methods, instead of training a policy function, we **train a value function** that maps a state to the expected value **of being at that state.**
+In value-based methods, instead of learning a policy function, we **learn a value function** that maps a state to the expected value **of being at that state.**
 
 The value of a state is the **expected discounted return** the agent can get if it **starts in that state, and then act according to our policy.**