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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Q-learning for FrozenLake

The whole example is in the Chapter05/ file, and the difference is really minor. The most obvious change is to our value table. In the previous example, we kept the value of the state, so the key in the dictionary was just a state. Now we need to store values of the Q-function, which has two parameters: state and action, so the key in the value table is now a composite.

The second difference is in our calc_action_value function. We just don't need it anymore, as our action values are stored in the value table. Finally, the most important change in the code is in the agent's value_iteration method. Before, it was just a wrapper around the calc_action_value call, which did the job of Bellman approximation. Now, as this function has gone and was replaced by a value table, we need to do this approximation in the value_iteration method.

Let's look at the code. As it's almost the same, I'll jump directly to the most interesting value_iteration...