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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Model imperfections

There is a serious issue with the model-based approach: when our model makes mistakes or is just inaccurate in some regimes of the environment, the policy learned from this model could be totally wrong in real-life situations. To deal with this, we have several options. The most obvious option is to "make the model better." Unfortunately, this can just mean that we'll need more observations from the environment, which is what we've tried to avoid. The more complicated and nonlinear the behavior that the environment has, the worse the situation will be for modelling it properly.

Several ways have been discovered to tackle this issue, for example, the local models family of methods, when we replace one large environment model with a small regime-based set of models and train them using trust-region tricks in the same way that T rust Region Policy Optimization (TRPO) does. Another interesting way of looking at environment models is to augment model-free policy with model...