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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Key elements of RL

Let's begin by understanding some key elements of RL.


An agent is a software program that learns to make intelligent decisions. We can say that an agent is a learner in the RL setting. For instance, a chess player can be considered an agent since the player learns to make the best moves (decisions) to win the game. Similarly, Mario in a Super Mario Bros video game can be considered an agent since Mario explores the game and learns to make the best moves in the game.


The environment is the world of the agent. The agent stays within the environment. For instance, coming back to our chess game, a chessboard is called the environment since the chess player (agent) learns to play the game of chess within the chessboard (environment). Similarly, in Super Mario Bros, the world of Mario is called the environment.

State and action

A state is a position or a moment in the environment that the agent can be in. We learned that...