Table of contents : 1: Introducing Machine Learning and ML-Agents Machine Learning ML-Agents Running a sample Creating an environment Academy, Agent, and Brain Summary
2: The Bandit and Reinforcement Learning Reinforcement Learning Contextual bandits and state Exploration and exploitation MDP and the Bellman equation Q-Learning and connected agents Exercises Summary
3: Deep Reinforcement Learning with Python Installing Python and tools ML-Agents external brains Neural network foundations Deep Q-learning Proximal policy optimization Exercises Summary
4: Going Deeper with Deep Learning Agent training problems Convolutional neural networks Experience replay Partial observability, memory, and recurrent networks Asynchronous actor – critic training Exercises Summary
5: Playing the Game Multi-agent environments Adversarial self-play Decisions and On-Demand Decision Making Imitation learning Curriculum Learning Exercises Summary
6: Terrarium Revisited – A Multi-Agent Ecosystem What was/is Terrarium? Building the Agent ecosystem Basic Terrarium – Plants and Herbivores Carnivore: the hunter Next steps Exercises Summary