Hands-On Intelligent Agents with OpenAI Gym

Hands-On Intelligent Agents with OpenAI Gym 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. It 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. (Limited-time offer)

Table of Contents

• 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)

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