Book Image

Deep Reinforcement Learning with Python - Second Edition

By : Sudharsan Ravichandiran
Book Image

Deep Reinforcement Learning with Python - Second Edition

By: Sudharsan Ravichandiran

Overview of this book

With significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit. In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples. The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI’s baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research. By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects.
Table of Contents (22 chapters)
Other Books You May Enjoy

Chapter 17 – Reinforcement Learning Frontiers

  1. Meta learning produces a versatile AI model that can learn to perform various tasks without having to train them from scratch. We train our meta-learning model on various related tasks with a few data points, so for a new but related task, it can make use of the learning obtained from the previous tasks and we don't have to train it from scratch.
  2. Model-Agnostic Meta Learning (MAML) is one of the most popularly used meta-learning algorithms and it has created a major breakthrough in meta-learning research. The basic idea of MAML is to find a better initial model parameter so that with good initial parameters, the model can learn quickly on new tasks with fewer gradient steps.
  3. In the outer loop of MAML, we update the model parameter as and it is known as a meta objective.
  4. The meta training set basically acts as a training set in the outer loop and is used to update the model parameter in the outer...