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)
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Chapter 14 – Distributional Reinforcement Learning

  1. In a distributional RL, instead of selecting an action based on the expected return, we select the action based on the distribution of the return, which is often called the value distribution or return distribution.
  2. In categorical DQN, we feed the state and support of the distribution as the input and the network returns the probabilities of the value distribution.
  3. The authors of the categorical DQN suggest that it will be efficient to choose the number of support N as 51 and so the categorical DQN is also known as the C51 algorithm.
  4. Inverse CDF is also known as the quantile function. Inverse CDF as the name suggests is the inverse of the cumulative distribution function. That is, in CDF, given the support x, we obtain the cumulative probability , whereas in inverse CDF, given cumulative probability , we obtain the support x.
  5. In a categorical DQN, along with the state, we feed the fixed support...