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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Why policy-based methods?

The objective of reinforcement learning is to find the optimal policy, which is the policy that provides the maximum return. So far, we have learned several different algorithms for computing the optimal policy, and all these algorithms have been value-based methods. Wait, what are value-based methods? Let's recap what value-based methods are, and the problems associated with them, and then we will learn about policy-based methods. Recapping is always good, isn't it?

With value-based methods, we extract the optimal policy from the optimal Q function (Q values), meaning we compute the Q values of all state-action pairs to find the policy. We extract the policy by selecting an action in each state that has the maximum Q value. For instance, let's say we have two states s0 and s1 and our action space has two actions; let the actions be 0 and 1. First, we compute the Q value of all the state-action pairs, as shown in the following table...