You're reading from Hands-On Intelligent Agents with OpenAI Gym

Product typeBook

Published inJul 2018

Reading LevelIntermediate

PublisherPackt

ISBN-139781788836579

Edition1st Edition

Languages

Python

Tools

OpenAI Gym

Concepts

Artificial Intelligence

Author (1)

Palanisamy P

Implementing a deep n-step advantage actor critic agent

We have prepared ourselves with all the background information required to implement the deep n-step advantage actor-critic (A2C) agent. Let's look at an overview of the agent implementation process and then jump right into the hands-on implementation.

The following is the high-level flow of our A2C agent:

Initialize the actor's and critic's networks.
Use the current policy of the actor to gather n-step experiences from the environment and calculate the n-step return.

Calculate the actor's and critic's losses.
Perform the stochastic gradent descent optimization step to update the actor and critic parameters.
Repeat from step 2.

We will implement the agent in a Python class named DeepActorCriticAgent. You will find the full implementation in this book's code repository under 8th chapter: ch8...

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Hands-On Intelligent Agents with OpenAI Gym

Published in: Jul 2018Publisher: PacktISBN-13: 9781788836579

Author (1)

Palanisamy P

Praveen Palanisamy works on developing autonomous intelligent systems. He is currently an AI researcher at General Motors R&D. He develops planning and decision-making algorithms and systems that use deep reinforcement learning for autonomous driving. Previously, he was at the Robotics Institute, Carnegie Mellon University, where he worked on autonomous navigation, including perception and AI for mobile robots. He has experience developing complete, autonomous, robotic systems from scratch.
Read more about Palanisamy P

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Author (1)

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