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
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Training code

We have two very similar training modules in this example: one for the feed-forward model and one for 1D convolutions. For both of them, there is nothing new added to our examples from Chapter 7, DQN Extensions:

  • They’re using epsilon-greedy action selection to perform exploration. The epsilon linearly decays over the first 1M steps from 1.0 to 0.1.

  • A simple experience replay buffer of size 100k is being used, which is initially populated with 10k transitions.

  • For every 1000 steps, we calculate the mean value for the fixed set of states to check the dynamics of the Q-values during the training.

  • For every 100k steps, we perform validation: 100 episodes are played on the training data and on previously unseen quotes. Characteristics of orders are recorded in TensorBoard, such as the mean profit, the mean count of bars, and share held. This step allows us to check for overfitting conditions.

The training modules are in Chapter08/ (feed-forward model) and Chapter08/train_model_conv...