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You're reading from  Mastering Reinforcement Learning with Python

Product typeBook
Published inDec 2020
Reading LevelBeginner
PublisherPackt
ISBN-139781838644147
Edition1st Edition
Languages
Python
Tools
PyTorch
Concepts
Reinforcement Learning
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Author (1)
Enes Bilgin
Enes Bilgin
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Enes Bilgin

Enes Bilgin works as a senior AI engineer and a tech lead in Microsoft's Autonomous Systems division. He is a machine learning and operations research practitioner and researcher with experience in building production systems and models for top tech companies using Python, TensorFlow, and Ray/RLlib. He holds an M.S. and a Ph.D. in systems engineering from Boston University and a B.S. in industrial engineering from Bilkent University. In the past, he has worked as a research scientist at Amazon and as an operations research scientist at AMD. He also held adjunct faculty positions at the McCombs School of Business at the University of Texas at Austin and at the Ingram School of Engineering at Texas State University.
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Deep Q-networks

DQN is a seminal work by (Mnih et al., 2015) that made deep RL a viable approach to complex sequential control problems. The authors demonstrated that a single DQN architecture can achieve super-human level performance in many Atari games without any feature engineering, which created a lot of excitement regarding the progress of AI. Let's look into what makes DQN so effective compared to the algorithms we mentioned earlier.

Key concepts in deep Q-networks

DQN modifies online Q-learning with two important concepts by using experience replay and a target network, which greatly stabilize the learning. We describe these concepts next.

Experience replay

As mentioned earlier, simply using the experience sampled sequentially from the environment leads to highly correlated gradient steps. DQN, on the other hand, stores those experience tuples, , in a replay buffer (memory), an idea that was introduced back in 1993 (Lin, 1993). During learning, the samples...

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Mastering Reinforcement Learning with Python
Published in: Dec 2020Publisher: PacktISBN-13: 9781838644147
© 2020 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Enes Bilgin

Enes Bilgin works as a senior AI engineer and a tech lead in Microsoft's Autonomous Systems division. He is a machine learning and operations research practitioner and researcher with experience in building production systems and models for top tech companies using Python, TensorFlow, and Ray/RLlib. He holds an M.S. and a Ph.D. in systems engineering from Boston University and a B.S. in industrial engineering from Bilkent University. In the past, he has worked as a research scientist at Amazon and as an operations research scientist at AMD. He also held adjunct faculty positions at the McCombs School of Business at the University of Texas at Austin and at the Ingram School of Engineering at Texas State University.
Read more about Enes Bilgin

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