Book Content
chapters • 8h28m total length
1. Introduction to Intelligent Agents and Learning Environments
2. Reinforcement Learning and Deep Reinforcement Learning
3. Getting Started with OpenAI Gym and Deep Reinforcement Learning
4. Exploring the Gym and its Features
5. Implementing your First Learning Agent – Solving the Mountain Car problem
6. Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning
7. Creating Custom OpenAI Gym Environments – Carla Driving Simulator
8. Implementing an Intelligent & Autonomous Car Driving Agent using Deep Actor-Critic Algorithm
9. Exploring the Learning Environment Landscape – Roboschool, Gym-Retro, StarCraft-II, DeepMindLab
10. Exploring the Learning Algorithm Landscape – DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based)
