Learning from human preference is a major breakthrough in RL. The algorithm was proposed by researchers at OpenAI and DeepMind. The idea behind the algorithm is to make the agent learn according to human feedback. Initially, the agents act randomly and then two video clips of the agent performing an action are given to a human. The human can inspect the video clips and tell the agent which video clip is better, that is, in which video the agent is performing the task better and will lead it to achieving the goal. Once this feedback is given, the agent will try to do the actions preferred by the human and set the reward accordingly. Designing reward functions is one of the major challenges in RL, so having human interaction with the agent directly helps us to overcome the challenge and also helps us to minimize the writing of complex goal functions...
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You're reading from Hands-On Reinforcement Learning with Python
Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.
Read more about Sudharsan Ravichandiran
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Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.
Read more about Sudharsan Ravichandiran