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

Summary

We started this chapter with the grand goal of developing intelligent learning agents that can achieve great scores in Atari games. We made incremental progress towards it by implementing several techniques to improve upon the Q-learner that we developed in the previous chapter. We first started with learning how we can use a neural network to approximate the Q action-value function and made our learning concrete by practically implementing a shallow neural network to solve the famous Cart Pole problem. We then implemented experience memory and experience replay that enables the agent to learn from (mini) randomly sampled batches of experiences that helped in improving the performance by breaking the correlations between the agent's interactions and increasing the sample efficiency with the batch replay of the agent's prior experience. We then revisited the epsilon...

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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)

TensorFlow 2 Reinforcement Learning Cookbook

TensorFlow Reinforcement Learning Quick Start Guide

PyTorch 1.x Reinforcement Learning Cookbook

Reinforcement Learning with TensorFlow

Hands-On Q-Learning with Python

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