Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.
Table of Contents (28 chapters)
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Further improvements and experiments

There are lots of directions and things that could be tried:

  • More input and network engineering: the cube is a complicated thing, so simple feed-forward NNs may not be the best model. Probably, the network could greatly benefit from convolutions.
  • Oscillations and instability during training might be a sign of a common RL issue with inter-step correlations. The usual approach is the target network, when we use the old version of the network to get bootstrapped values.
  • The priority replay buffer might help the training speed.
  • My experiments show that the samples' weighting (inversely proportional to the scramble depth) helps to get a better policy that knows how to solve slightly scrambled cubes, but might slow down the learning of deeper states. Probably, this weighting could be made adaptive to make it less aggressive in later training stages.
  • Entropy loss could be added to the training to regularize our policy.
  • ...