- For an approach that uses a pure curiosity-driven approach in the Atari games, read the paper Large-scale study of curiosity-driven learning (https://arxiv.org/pdf/1808.04355.pdf).
- For practical use of domain randomization for learning dexterous in-hand manipulation, read the paper Learning Dexterous In-Hand Manipulation (https://arxiv.org/pdf/1808.00177.pdf).
- For some work that shows how human feedback can be applied as an alternative to the reward function, read the paper Deep Reinforcement Learning from Policy-Dependent Human Feedback (https://arxiv.org/pdf/1902.04257.pdf).
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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