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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Combining everything

We've now seen all DQN improvements mentioned in the paper [1] Rainbow: Combining Improvements in Deep Reinforcement Learning. Let's combine all of them into one hybrid method. First of all, we need to define our network architecture and the three methods that have contributed to it:

  • Categorical DQN: Our network will predict the value probability distribution of actions.

  • Dueling DQN: Our network will have two separate paths for value of state distribution and advantage distribution. On the output, both paths will be summed together, providing the final value probability distributions for actions. To force advantage distribution to have a zero mean, we'll subtract distribution with mean advantage in every atom.

  • NoisyNet: Our linear layers in the value and advantage paths will be noisy variants of nn.Linear.

In addition to network architecture changes, we'll use prioritized replay buffer to keep environment transitions and sample them proportionally to KL-divergence. Finally...