Book Image

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On

By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
Table of Contents (23 chapters)
Deep Reinforcement Learning Hands-On
Contributors
Preface
Other Books You May Enjoy
Index

Imagination-augmented agent


The overall idea of the new architecture called imagination-augmented agent (I2A) is to allow the agent to imagine future trajectories by the current observations and incorporate these imagined paths into its decision process. The high-level architecture is shown in the following diagram:

Figure 1: 12A architecture

The agent consists of two different paths used to transform the input observation: model-free and imagination. Model-free is a standard set of convolution layers transforming the input image in high-level features. Another path is called imagination and consists of a set of trajectories "imagined" from the current observation. The trajectories are called rollouts and are produced for every available action in the environment. Every rollout consists of a fixed number of steps into the future and on every step a special model, called the Environment Model (EM), (but not to be confused with the expectation maximization method), produces the next observation...