OpenAI Universe is available in OpenAI's GitHub repository https://github.com/openai/universe and its core idea is to wrap general GUI applications into an RL environment using the same core classes provided by Gym. To achieve this, it uses the VNC protocol to connect with the VNC server running inside the docker container, exposing the mouse and keyboard actions to the RL agent and providing the GUI application image as an observation. The reward is provided by an external small "rewarder" daemon running inside the same container and giving the agent scalar reward value based on this rewarder judgement. It is possible to launch several containers locally, or over the network, to gather episodes data in parallel, in the same way that we started several Atari emulators to increase the convergence of the Actor-Critic (A2C) method in Chapter 11, Asynchronous Advantage Actor‑Critic. The architecture is illustrated in the following diagram:
Deep Reinforcement Learning Hands-On
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Deep Reinforcement Learning Hands-On
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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
Free Chapter
What is Reinforcement Learning?
OpenAI Gym
Deep Learning with PyTorch
The Cross-Entropy Method
Tabular Learning and the Bellman Equation
Deep Q-Networks
DQN Extensions
Stocks Trading Using RL
Policy Gradients – An Alternative
The Actor-Critic Method
Asynchronous Advantage Actor-Critic
Chatbots Training with RL
Web Navigation
Continuous Action Space
Trust Regions – TRPO, PPO, and ACKTR
Black-Box Optimization in RL
Beyond Model-Free – Imagination
AlphaGo Zero
Index
Customer Reviews