Book Image

Python Reinforcement Learning

By : Sudharsan Ravichandiran, Sean Saito, Rajalingappaa Shanmugamani, Yang Wenzhuo
Book Image

Python Reinforcement Learning

By: Sudharsan Ravichandiran, Sean Saito, Rajalingappaa Shanmugamani, Yang Wenzhuo

Overview of this book

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. This Learning Path will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The Learning Path starts with an introduction to RL followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. You'll also work on various datasets including image, text, and video. This example-rich guide will introduce you to deep RL algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore TensorFlow and OpenAI Gym to implement algorithms that also predict stock prices, generate natural language, and even build other neural networks. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many of the recent advancements in RL. By the end of the Learning Path, you will have all the knowledge and experience needed to implement RL and deep RL in your projects, and you enter the world of artificial intelligence to solve various real-life problems. This Learning Path includes content from the following Packt products: • Hands-On Reinforcement Learning with Python by Sudharsan Ravichandiran • Python Reinforcement Learning Projects by Sean Saito, Yang Wenzhuo, and Rajalingappaa Shanmugamani
Table of Contents (27 chapters)
Title Page
About Packt
Contributors
Preface
Index

Asynchronous advantage actor-critic algorithm


In the previous chapters, we discussed the DQN for playing Atari games and the use of the DPG and TRPO algorithms for continuous control tasks. Recall that DQN has the following architecture:

At each timestep

, the agent observes the frame image 

and selects an action 

based on the current learned policy. The emulator (the Minecraft environment) executes this action and returns the next frame image 

and the corresponding reward

. The quadruplet 

is then stored in the experience memory and is taken as a sample for training the Q-network by minimizing the empirical loss function via stochastic gradient descent.

Deep reinforcement learning algorithms based on experience replay have achieved unprecedented success in playing Atari games. However, experience replay has several disadvantages:

  • It uses more memory and computation per real interaction
  • It requires off-policy learning algorithms that can update from data generated by an older policy

In order...