You're reading from Hands-On Reinforcement Learning with Python

Product typeBook

Published inJun 2018

Reading LevelIntermediate

PublisherPackt

ISBN-139781788836524

Edition1st Edition

Languages

Python

Tools

TensorFlow Keras

Concepts

Reinforcement Learning

Author (1)

Sudharsan Ravichandiran

Chapter 13

Imagination in an agent specifies visualizing and planning before taking any action.
Imagination core consists of policy network and environmental model for performing imagination.
Agents repeatedly take feedback from the human and change its goal according to the human preference.
DQfd uses some demonstration data for training where as DQN doesn't use any demonstrations data upfront.
Refer section Hindsight Experience Replay (HER).

Hierarchical reinforcement learning (HRL) is proposed to solve the curse of dimensionality where we decompress large problems into small subproblems in a hierarchy
We tried to find the optimal policy given the reward function in RL whereas in inverse reinforcement learning, the optimal policy is given and we find the reward function

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Hands-On Reinforcement Learning with Python

Published in: Jun 2018Publisher: PacktISBN-13: 9781788836524

Author (1)

Sudharsan Ravichandiran

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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Author (1)

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