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
Other Books You May Enjoy

I2A on Atari Breakout

The code and training path of I2A is a bit complicated and includes lots of code and several steps. To understand it better, let's start with a brief overview. In this example, we'll implement the I2A architecture described in the paper, adopted to the Atari environments, and test it on the Breakout game. The overall goal is to check the training dynamics and the effect of imagination augmentation on the final policy.

Our example consists of three parts, which correspond to different steps in the training:

  1. Baseline A2C agent in Chapter17/ The resulting policy is used for obtaining observations of the environment model.

  2. Environment model training in Chapter17/ It uses the model obtained on the previous step to train EM in an unsupervised way. The result is EM weights.

  3. The final I2A agent training in Chapter17/ In this step, we use the EM from step 2 to train a full I2A agent, which combines the model-free and rollouts paths.

Due to size of the...