🐛 FIX: Cross-reference for training agents not working fixed (#1493)

Author: Ahlirek

docs/introduction/record_agent.md

@@ -299,7 +299,7 @@ for episode_num in range(num_training_episodes):
 
 * [Training an agent](train_agent) - Learn how to build the agents you're recording
 * [Basic usage](basic_usage) - Understand Gymnasium fundamentals
-* [More training tutorials](../tutorials/training_agents) - Advanced training techniques
+* {doc}`More training tutorials </tutorials/training_agents/index>` - Advanced training techniques
 * [Custom environments](create_custom_env) - Create your own environments to record
 
 Recording agent behavior is an essential skill for RL practitioners. It helps you understand what your agent is actually learning, debug training issues, and communicate results effectively. Start with simple recording setups and gradually add more sophisticated analysis as your projects grow in complexity!

docs/introduction/train_agent.md

@@ -35,7 +35,7 @@ For Blackjack:
 
 ---
 
-This page provides a short outline of how to train an agent for a Gymnasium environment. We'll use tabular Q-learning to solve Blackjack-v1. For complete tutorials with other environments and algorithms, see [training tutorials](../tutorials/training_agents). Please read [basic usage](basic_usage) before this page.
+This page provides a short outline of how to train an agent for a Gymnasium environment. We'll use tabular Q-learning to solve Blackjack-v1. For complete tutorials with other environments and algorithms, see {doc}`training tutorials </tutorials/training_agents/index>`. Please read [basic usage](basic_usage) before this page.
 
 ## About the Environment: Blackjack
 
@@ -438,7 +438,7 @@ For more information, see:
 
 * [Basic Usage](basic_usage) - Understanding Gymnasium fundamentals
 * [Custom Environments](create_custom_env) - Building your own RL problems
-* [Complete Training Tutorials](../tutorials/training_agents) - More algorithms and environments
+* {doc}`Complete Training Tutorials </tutorials/training_agents/index>` - More algorithms and environments
 * [Recording Agent Behavior](record_agent) - Saving videos and performance data
 
 The key insight from this tutorial is that RL agents learn through trial and error, gradually building up knowledge about what actions work best in different situations. Q-learning provides a systematic way to learn this knowledge, balancing exploration of new possibilities with exploitation of current knowledge.