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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Tabular Q-learning

First of all, do we really need to iterate over every state in the state space? We have an environment that can be used as a source of real-life samples of states. If some state in the state space is not shown to us by the environment, why should we care about its value? We can use states obtained from the environment to update values of states, which can save us lots of work.

This modification of the Value iteration method is known as Q-learning, as mentioned earlier, and for cases with explicit state-to-value mappings, has the following steps:

  1. Start with an empty table, mapping states to values of actions.

  2. By interacting with the environment, obtain the tuple s, a, r, s′ (state, action, reward, and the new state). In this step, we need to decide which action to take, and there is no single proper way to make this decision. We discussed this problem as exploration versus exploitation and will talk a lot about this.

  3. Update the Q(s, a) value using the Bellman approximation...