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)
18
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Index

Monte Carlo Methods

In the previous chapter, we learned how to compute the optimal policy using two interesting dynamic programming methods called value and policy iteration. Dynamic programming is a model-based method and it requires the model dynamics of the environment to compute the value and Q functions in order to find the optimal policy.

But let's suppose we don't have the model dynamics of the environment. In that case, how do we compute the value and Q functions? Here is where we use model-free methods. Model-free methods do not require the model dynamics of the environment to compute the value and Q functions in order to find the optimal policy. One such popular model-free method is the Monte Carlo (MC) method.

We will begin the chapter by understanding what the MC method is, then we will look into two important types of tasks in reinforcement learning called prediction and control tasks. Later, we will learn how the Monte Carlo method is used...