Equipped with all the technical knowledge about Q-learning, deep neural networks, and DQN, we can finally put it to work and start to warm up the GPU. In this section, we will apply DQN to an Atari environment, Pong. We have chosen Pong rather than all the other Atari environments because it's simpler to solve and thus requires less time, computational power, and memory. That being said, if you have a decent GPU available, you can apply the same exact configuration to almost all the other Atari games (some may require a little bit of fine-tuning). For the same reason, we adopted a lighter configuration compared to the original DQN paper, both in terms of the capacity of the function approximator (that is, fewer weights) and hyperparameters such as a smaller buffer size. This does not compromise the results rather on Pong but might degrade the performance...
- Tech Categories
- Best Sellers
- New Releases
- Books
- Videos
- Audiobooks
Tech Categories Popular Audiobooks
- Articles
- Newsletters
- Free Learning
You're reading from Reinforcement Learning Algorithms with Python
Andrea Lonza is a deep learning engineer with a great passion for artificial intelligence and a desire to create machines that act intelligently. He has acquired expert knowledge in reinforcement learning, natural language processing, and computer vision through academic and industrial machine learning projects. He has also participated in several Kaggle competitions, achieving high results. He is always looking for compelling challenges and loves to prove himself.
Read more about Andrea Lonza
Unlock this book and the full library FREE for 7 days
Author (1)
Andrea Lonza is a deep learning engineer with a great passion for artificial intelligence and a desire to create machines that act intelligently. He has acquired expert knowledge in reinforcement learning, natural language processing, and computer vision through academic and industrial machine learning projects. He has also participated in several Kaggle competitions, achieving high results. He is always looking for compelling challenges and loves to prove himself.
Read more about Andrea Lonza