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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Chapter 1 – Fundamentals of Reinforcement Learning

  1. In supervised and unsupervised learning, the model (agent) learns based on the given training dataset, whereas, in reinforcement learning (RL), the agent learns by directly interacting with the environment. Thus RL is essentially an interaction between the agent and its environment.
  2. The environment is the world of the agent. The agent stays within the environment. For instance, in the chess game, the chessboard is the environment since the chess player (agent) learns to play chess within the chessboard (environment). Similarly, in the Super Mario Bros game, the world of Mario is called the environment.
  3. The deterministic policy maps the state to one particular action, whereas the stochastic policy maps the state to the probability distribution over an action space.
  4. The agent interacts with the environment by performing actions, starting from the initial state until they reach the final state...