Book Image

Hands-On Reinforcement Learning with Python

By : Sudharsan Ravichandiran
Book Image

Hands-On Reinforcement Learning with Python

By: Sudharsan Ravichandiran

Overview of this book

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.
Table of Contents (16 chapters)

Chapter 4

  1. The Monte Carlo algorithm is used in RL when the model of the environment is not known.
  2. Refer section Estimating the value of pi using Monte Carlo.
  3. In Monte Carlo prediction, we approximate the value function by taking the mean return instead of the expected return.
  4. In every visit Monte Carlo, we average the return every time the state is visited in an episode. But in the first visit MC method, we average the return only the first time the state is visited in an episode.

  1. Refer section Monte Carlo control.
  2. Refer section On-policy Monte Carlo control and Off-policy Monte Carlo control
  3. Refer section Let's play Blackjack with Monte Carlo.