fix: links (#24)

_data/authors.yml

@@ -4,7 +4,7 @@ davide:
   url_full: https://github.com/DavideTr8
   url: davidetr8.github.io
   bio: I'm graduated student at the University of Padua, Italy. I'm studying Math and I'm interested in Optimization and Reinfocement Learning.
-  picture: assets/images/ghost.png
+  # picture: assets/images/ghost.png
   linkedin: https://www.linkedin.com/in/davide-angioni/
   facebook:
   twitter:

_posts/2023-10-10-from_0_to_rl.md

@@ -21,9 +21,9 @@ One of the standout features of SheepRL is its versatility in handling different
 
 What truly sets SheepRL apart is its commitment to staying at the cutting edge of RL algorithms. The inclusion of state-of-the-art algorithms like [Dreamer-V3](https://eclecticsheep.ai/2023/08/10/dreamer_v3.html), demonstrates SheepRL's dedication to providing users with the most advanced and effective tools available in the field. Dreamer-V3, in particular, showcases the framework's capability to simulate and learn from imagined experiences, a feature that opens up exciting possibilities for training RL agents.
 
-SheepRL's fully distributed nature, made possible by [Lightning Fabric](lightning.ai/docs/fabric/stable/), ensures that RL practitioners can harness the power of distributed computing seamlessly. This distributed approach enables more efficient training of RL models, making it suitable for tackling complex real-world problems that demand substantial computational resources.
+SheepRL's fully distributed nature, made possible by [Lightning Fabric](https://lightning.ai/docs/fabric/stable/), ensures that RL practitioners can harness the power of distributed computing seamlessly. This distributed approach enables more efficient training of RL models, making it suitable for tackling complex real-world problems that demand substantial computational resources.
 
-Moreover, SheepRL's adaptability is further enhanced by its integration with [Hydra](hydra.cc). This integration simplifies the process of configuring and customizing RL experiments, making it user-friendly and accessible to a wide range of developers, from beginners to experts. The framework's emphasis on clarity and ease of use is a testament to its commitment to fostering a welcoming and productive RL development environment.
+Moreover, SheepRL's adaptability is further enhanced by its integration with [Hydra](https://hydra.cc/). This integration simplifies the process of configuring and customizing RL experiments, making it user-friendly and accessible to a wide range of developers, from beginners to experts. The framework's emphasis on clarity and ease of use is a testament to its commitment to fostering a welcoming and productive RL development environment.
 
 ### Dreamer-V3 beats them all!
 

_posts/2023-12-14-intro-rl.md

@@ -506,7 +506,7 @@ def main(fabric: Fabric, cfg: Dict[str, Any]):
 
 #### 2. agent\.py
 
-The second thing is the **agent.py** file, where we will define the **build_agent** function, which will be used to create the agent model. We can take inspiration from the [PPO agent.py file](https://github.com/Eclectic-Sheep/sheeprl/blob/feature/buffer-np/sheeprl/algos/ppo/ppo.py) and, to keep thing at a demonstration level, remove everything regarding the encoding/processing of pixel-based observations:
+The second thing is the **agent.py** file, where we will define the **build_agent** function, which will be used to create the agent model. We can take inspiration from the [PPO agent.py file](https://github.com/Eclectic-Sheep/sheeprl/blob/main/sheeprl/algos/ppo/ppo.py) and, to keep thing at a demonstration level, remove everything regarding the encoding/processing of pixel-based observations:
 
 <button type="button" class="collapsible">a2c/agent.py</button>
 <div class="collapsible-content">
@@ -1028,4 +1028,4 @@ where *log_dir* is the directory where the logs are saved, which is printed in t
 
 ## Conclusion
 
-In this article we have seen how to implement the A2C algorithm in SheepRL. In particular, we have seen how to define the algorithm, starting from the [**register-new-algorithm how-to**](https://github.com/Eclectic-Sheep/sheeprl/blob/feature/buffer-np/howto/register_new_algorithm.md), the agent by taking inspiration from the [PPO one](https://github.com/Eclectic-Sheep/sheeprl/blob/feature/buffer-np/sheeprl/algos/ppo/ppo.py), the loss functions, the utility functions, and the configuration files. We have also seen how to register the algorithm and how to run the experiment. We hope you have enjoyed this article and we hope to see you in the next one!
+In this article we have seen how to implement the A2C algorithm in SheepRL. In particular, we have seen how to define the algorithm, starting from the [**register-new-algorithm how-to**](https://github.com/Eclectic-Sheep/sheeprl/blob/main/howto/register_new_algorithm.md), the agent by taking inspiration from the [PPO one](https://github.com/Eclectic-Sheep/sheeprl/blob/main/sheeprl/algos/ppo/ppo.py), the loss functions, the utility functions, and the configuration files. We have also seen how to register the algorithm and how to run the experiment. We hope you have enjoyed this article and we hope to see you in the next one!