In this chapter, we got hands-on with an actor-critic architecture-based deep reinforcement learning agent, starting from the basics. We started with the introduction to policy gradient-based methods and walked through the step-by-step process of representing the objective function for the policy gradient optimization, understanding the likelihood ratio trick, and finally deriving the policy gradient theorem. We then looked at how the actor-critic architecture makes use of the policy gradient theorem and uses an actor component to represent the policy of the agent, and a critic component to represent the state/action/advantage value function, depending on the implementation of the architecture. With an intuitive understanding of the actor-critic architecture, we moved on to the A2C algorithm and discussed the six steps involved in it. We then discussed the n-step return...
Hands-On Intelligent Agents with OpenAI Gym
By :
Hands-On Intelligent Agents with OpenAI Gym
By:
Overview of this book
Many real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken. The ability to solve such tasks without a machine being programmed requires a machine to be artificially intelligent and capable of learning to adapt. This book is an easy-to-follow guide to implementing learning algorithms for machine software agents in order to solve discrete or continuous sequential decision making and control tasks.
Hands-On Intelligent Agents with OpenAI Gym takes you through the process of building intelligent agent algorithms using deep reinforcement learning starting from the implementation of the building blocks for configuring, training, logging, visualizing, testing, and monitoring the agent. You will walk through the process of building intelligent agents from scratch to perform a variety of tasks. In the closing chapters, the book provides an overview of the latest learning environments and learning algorithms, along with pointers to more resources that will help you take your deep reinforcement learning skills to the next level.
Related Content you might be interested in
Current Title:
Hands-On Intelligent Agents with OpenAI Gym
Table of Contents (12 chapters)
Preface
Free Chapter
Introduction to Intelligent Agents and Learning Environments
Reinforcement Learning and Deep Reinforcement Learning
Getting Started with OpenAI Gym and Deep Reinforcement Learning
Exploring the Gym and its Features
Implementing your First Learning Agent - Solving the Mountain Car problem
Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning
Creating Custom OpenAI Gym Environments - CARLA Driving Simulator
Implementing an Intelligent - Autonomous Car Driving Agent using Deep Actor-Critic Algorithm
Exploring the Learning Environment Landscape - Roboschool, Gym-Retro, StarCraft-II, DeepMindLab
Exploring the Learning Algorithm Landscape - DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based)
Other Books You May Enjoy
Customer Reviews