- If you are interested in the original paper of the NPG, read A Natural Policy Gradient: https://papers.nips.cc/paper/2073-a-natural-policy-gradient.pdf.
- For the paper that introduced the Generalized Advantage Function, please read High-Dimensional Continuous Control Using Generalized Advantage Estimation: https://arxiv.org/pdf/1506.02438.pdf.
- If you are interested in the original Trust Region Policy Optimization paper, then please read Trust Region Policy Optimization: https://arxiv.org/pdf/1502.05477.pdf.
- If you are interested in the original paper that introduced the Proximal Policy Optimization algorithm, then please read Proximal Policy Optimization Algorithms: https://arxiv.org/pdf/1707.06347.pdf.
- For a further explanation of Proximal Policy Optimization, read the following blog post: https://openai.com/blog/openai-baselines-ppo/.
- If you are interested in...
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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.
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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