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

Categorical DQN

In the last section, we learned why it is more beneficial to choose an action based on the distribution of return than to choose an action based on the Q value, which is just the expected return. In this section, we will understand how to compute the distribution of return using an algorithm called categorical DQN.

The distribution of return is often called the value distribution or return distribution. Let Z be the random variable and Z(s, a) denote the value distribution of a state s and an action a. We know that the Q function is represented by Q(s, a) and it gives the value of a state-action pair. Similarly, now we have Z(s, a) and it gives the value distribution (return distribution) of the state-action pair.

Okay, how can we compute Z(s, a)? First, let's recollect how we compute Q(s, a).

In DQN, we learned that we use a neural network to approximate the Q function, Q(s, a), Since we use a neural network to approximate the Q function, we can represent...