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)

Recent Advancements and Next Steps

Congratulations! You have made it to the final chapter. We have come a long way! We started off with the very basics of RL, such as MDP, Monte Carlo methods, and TD learning and moved on to advanced deep reinforcement learning algorithms such as DQN, DRQN, and A3C. We have also learned about interesting state-of-the-art policy gradient methods such as DDPG, PPO, and TRPO, and we built a car-racing agent as our final project. But RL still has a lot more for us to explore, with increasing advancements each and every day. In this chapter, we will learn about some of the advancement in RL followed by hierarchical and inverse RL.

In this chapter, you will learn the following:

  • Imagination augmented agents (I2A)
  • Learning from human preference
  • Deep Q learning from demonstrations
  • Hindsight experience replay
  • Hierarchical reinforcement learning
  • Inverse...