Fix small typo in docs (#1241)

docs/introduction/train_agent.md

@@ -7,7 +7,7 @@ title: Train an Agent
 
 This page provides a short outline of how to train an agent for a Gymnasium environment, in particular, we will use a tabular based Q-learning to solve the Blackjack v1 environment. For a full complete version of this tutorial and more training tutorials for other environments and algorithm, see [this](../tutorials/training_agents). Please read [basic usage](basic_usage) before reading this page. Before we implement any code, here is an overview of Blackjack and Q-learning.
 
-Blackjack is one of the most popular casino card games that is also infamous for being beatable under certain conditions. This version of the game uses an infinite deck (we draw the cards with replacement), so counting cards won't be a viable strategy in our simulated game. The observation is a tuple of the player's current sum, the value of the dealers face-up card and a boolean value on whether the player holds a usable case. The agent can pick between two actions: stand (0) such that the player takes no more cards and hit (1) such that the player will take another player. To win, your card sum should be greater than the dealers without exceeding 21. The game ends if the player selects stand or if the card sum is greater than 21. Full documentation can be found at [https://gymnasium.farama.org/environments/toy_text/blackjack](https://gymnasium.farama.org/environments/toy_text/blackjack).
+Blackjack is one of the most popular casino card games that is also infamous for being beatable under certain conditions. This version of the game uses an infinite deck (we draw the cards with replacement), so counting cards won't be a viable strategy in our simulated game. The observation is a tuple of the player's current sum, the value of the dealers face-up card and a boolean value on whether the player holds a usable case. The agent can pick between two actions: stand (0) such that the player takes no more cards and hit (1) such that the player will take another card. To win, your card sum should be greater than the dealers without exceeding 21. The game ends if the player selects stand or if the card sum is greater than 21. Full documentation can be found at [https://gymnasium.farama.org/environments/toy_text/blackjack](https://gymnasium.farama.org/environments/toy_text/blackjack).
 
 Q-learning is a model-free off-policy learning algorithm by Watkins, 1989 for environments with discrete action spaces and was famous for being the first reinforcement learning algorithm to prove convergence to an optimal policy under certain conditions.