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)

Summary

In this chapter, we have learned about several recent advancements in RL. We saw how I2A architecture uses the imagination core for forward planning followed by how agents can be trained according to human preference. We also learned about DQfd, which boosts the performance and reduces the training time of DQN by learning from demonstrations. Then we looked at hindsight experience replay where we learned how agents learn from failures.

Next, we learned about hierarchical RL, where the goal is decompressed into a hierarchy of sub-goals. We learned about inverse RL where the agents try to learn the reward function given the policy. RL is evolving each and every day with interesting advancements; now that you have understood various reinforcement learning algorithms, you can build agents to perform various tasks and contribute to RL research.

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