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 13

  1. Imagination in an agent specifies visualizing and planning before taking any action.
  2. Imagination core consists of policy network and environmental model for performing imagination.
  3. Agents repeatedly take feedback from the human and change its goal according to the human preference.
  4. DQfd uses some demonstration data for training where as DQN doesn't use any demonstrations data upfront.
  5. Refer section Hindsight Experience Replay (HER).
  1. Hierarchical reinforcement learning (HRL) is proposed to solve the curse of dimensionality where we decompress large problems into small subproblems in a hierarchy
  2. We tried to find the optimal policy given the reward function in RL whereas in inverse reinforcement learning, the optimal policy is given and we find the reward function