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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Understanding the Monte Carlo method

Before understanding how the Monte Carlo method is useful in reinforcement learning, first, let's understand what the Monte Carlo method is and how it works. The Monte Carlo method is a statistical technique used to find an approximate solution through sampling.

For instance, the Monte Carlo method approximates the expectation of a random variable by sampling, and when the sample size is greater, the approximation will be better. Let's suppose we have a random variable X and say we need to compute the expected value of X; that is E(X), then we can compute it by taking the sum of the values of X multiplied by their respective probabilities as follows:

But instead of computing the expectation like this, can we approximate it with the Monte Carlo method? Yes! We can estimate the expected value of X by just sampling the values of X for some N times and compute the average value of X as the expected value of X as follows: